SYSTEM AND METHOD FOR CONTEXTUAL APPEARANCE ANALYSIS AND RECOMMENDATIONS

Abstract

SYSTEM AND METHOD FOR CONTEXTUAL APPEARANCE ANALYSIS AND RECOMMENDATIONS ABSTRACT A system (102) for contextual appearance analysis includes a processor (104) configured to receive a digital image of a subject and contextual information specifying the subject's appearance context. The processor (104) accesses an appearance standards database containing criteria for various appearance contexts. The processor (104) segments the subject from the background, detects and analyses multiple parameters based on the provided context, and compares these parameters with context-specific standards. The processor (104) then generates an appearance score by quantifying discrepancies between the analysed parameters and the standards. The system (100) also provides recommendations for improvement and controls an output interface to display the appearance score, discrepancies, and suggestions for achieving compliance with the context-specific standards. FIG. 1

Specification

Description:TECHNICAL FIELD
The present disclosure relates to image processing and analysis systems; and more specifically, to a system and a method for contextual appearance analysis and recommendations.
BACKGROUND
In various industries, maintaining adherence to appearance and safety standards is crucial for ensuring compliance with organizational norms and enhancing customer satisfaction. Sectors such as airlines, hospitality, retail, and oil refineries, among others, place significant importance on meeting specific standards. These standards can range from professional attire and grooming for air hostesses to dental alignment for healthcare professionals, and appropriate safety equipment for workers in hazardous environments. The need to assess compliance with these standards extends beyond mere physical appearance, encompassing a broad spectrum of criteria relevant to each industry.
Current methods of appearance assessment are predominantly manual, relying on supervisors or managers to evaluate staff based on predefined guidelines. These evaluations are often time-consuming and prone to human bias, leading to inconsistencies in enforcement and feedback. Automated systems for image analysis do exist, but they typically lack the ability to incorporate context-specific criteria, limiting their applicability in industries where the appearance and safety standards are dynamic and multifaceted. Furthermore, existing systems that provide recommendations for appearance improvement often offer generic advice, failing to tailor their suggestions to the individual or the specific context in which the appearance is being evaluated.
Therefore, in light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks.
SUMMARY
The present disclosure provides a system and a method for contextual appearance analysis. The present disclosure provides a solution to the existing problem of how to objectively and accurately assess adherence to appearance and safety standards across various industries, including airlines, hospitality, retail, healthcare, and industrial sectors. An aim of the present disclosure is to provide a solution that overcomes at least partially the problems encountered in prior art and provides an improved system that integrates context-specific criteria, performs detailed image analysis, and delivers customized recommendations tailored to individual requirements and industry standards. The present disclosure further provides an improved method that not only automates the evaluation process but also adapts to dynamic and multifaceted standards, ensuring consistent, reliable, and actionable feedback for users and organizations.
One or more objectives of the present disclosure is achieved by the solutions provided in the enclosed independent claims. Advantageous implementations of the present disclosure are further defined in the dependent claims.
In one aspect, the present disclosure provides a system for contextual appearance analysis. The system includes a processor configured to receive a digital image of a subject and contextual information specifying an appearance context of the subject, via an input interface. The processor is further configured to access an appearance standards database containing criteria for a plurality of appearance contexts comprising the appearance context of the subject. The processor is further configured to segment the subject from a background in the digital image. The processor is further configured to detect and analyze a plurality of parameters associated with the subject in the digital image based on the contextual information. The processor is further configured to compare the analysed parameters with context-specific standards received from the appearance standards database. The processor is further configured to generate an appearance score by quantifying discrepancies between the analysed parameters and the context-specific standards and generate recommendations for improvement based on the comparison. The processor is further configured to control display, via an output interface, to provide the appearance score, quantified discrepancies between the analysed parameters and the context-specific standards, and the recommendations for achieving compliance with the context-specific standards.
By leveraging advanced image segmentation techniques and contextual analysis, the system of the present disclosure provides a versatile and efficient solution for assessing appearance across various scenarios. The technical effect of segmenting the subject from the background in the digital image allows for precise isolation of the relevant elements, reducing noise and improving the accuracy of subsequent analysis. This segmentation, combined with the detection and analysis of multiple parameters based on the provided contextual information, enables the system to focus on the most pertinent aspects of the subject's appearance for the given context. The system's ability to access and utilize the appearance standards database containing criteria for multiple contexts represents a significant technical advantage. This feature allows for dynamic and flexible analysis, adapting to different appearance scenarios without requiring separate specialized systems for each context. The comparison of analysed parameters with context-specific standards enables the system to provide highly relevant and accurate assessments. The generation of an appearance score through the quantification of discrepancies between analysed parameters and context-specific standards represents a technical advancement in objectifying appearance analysis. This scoring mechanism, coupled with the generation of tailored recommendations for improvement, provides actionable insights based on complex image analysis and data comparison processes. The system's capability to control the display of these results, including the appearance score, quantified discrepancies, and recommendations, through an output interface, ensures clear and comprehensive communication of the analysis results.
Furthermore, the system of the present disclosure allows for potential integration with machine learning operations, enabling continuous improvement in accuracy and relevance of assessments over time. The technical architecture of the system, with its modular approach to input processing, database access, image analysis, and output generation, provides a scalable and adaptable solution that can evolve with advancing technologies and changing appearance standards across various contexts.
In summary, the system of the present disclosure represents a technically sophisticated approach to appearance analysis, offering a versatile, accurate, and user-friendly solution that can be applied across a wide range of scenarios, from professional environments to personal grooming, fashion, and beyond. Its ability to provide objective, context-specific assessments and recommendations marks a significant advancement in automated appearance evaluation technology.
In another aspect, the present disclosure provides a method implemented in a system for contextual appearance analysis. The method includes receiving, by at least one processor, a digital image of a subject and contextual information specifying an appearance context, via an input interface. The method further includes accessing, by the at least one processor, an appearance standards database containing criteria for a plurality of appearance contexts. The method further includes segmenting, by the at least one processor the subject from a background in the digital image. The method further includes detecting and analyzing, by the at least one processor, a plurality of parameters associated with the subject in the digital image based on the contextual information. The method further includes comparing, by the at least one processor, the analysed parameters with context-specific standards received from the appearance standards database. The method further includes generating, by the at least one processor, an appearance score by quantifying discrepancies between the analysed parameters and the context-specific standards, and generating, by the at least one processor, recommendations for improvement based on the comparison. The method further includes controlling, by the at least one processor, display to provide the appearance score, the quantified discrepancies between the analysed parameters and the context-specific standards, and the recommendations for achieving compliance with the context-specific standards, via an output interface.
The method achieves all the advantages and technical effects of the system of the present disclosure.
It is to be appreciated that all the aforementioned implementation forms can be combined. All steps which are performed by the various entities described in the present application as well as the functionalities described to be performed by the various entities are intended to mean that the respective entity is adapted to or configured to perform the respective steps and functionalities. It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims. Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative implementations construed in conjunction with the appended claims that follow.
BRIEF DESCRIPTION OF THE DRAWINGS
The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those skilled in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.
Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:
FIG. 1 is a block diagram illustrating a system for contextual appearance analysis, in accordance with an embodiment of the present disclosure;
FIGs. 2A-2D are schematic diagrams illustrating a series of operations for contextual appearance analysis, in accordance with an embodiment of the present disclosure; and
FIG. 3 is a flowchart of a method for contextual appearance analysis, in accordance with an embodiment of the present disclosure.
DETAILED DESCRIPTION OF EMBODIMENTS
The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practicing the present disclosure are also possible.
As used throughout this disclosure, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words "include", "including", and "includes" mean including but not limited to.
The phrases "at least one", "one or more", and "and/or" are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions "at least one of A, B and C", "at least one of A, B, or C", "one or more of A, B, and C", "one or more of A, B, or C" and "A, B, and/or C" means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B and C together.
The term "a" or "an" entity refers to one or more of that entity. As such, the terms "a" (or "an"), "one or more" and "at least one" can be used interchangeably herein. It is also to be noted that the terms "comprising", "including", and "having" can be used interchangeably.
The term "automatic" and variations thereof, as used herein, refers to any process or operation done without material human input when the process or operation is performed. However, a process or operation can be automatic, even though performance of the process or operation uses material or immaterial human input, if the input is received before performance of the process or operation. Human input is deemed to be material if such input influences how the process or operation will be performed. Human input that consents to the performance of the process or operation is not deemed to be "material".
The present subject matter may have a variety of modifications and may be embodied in a variety of forms, and specific embodiments will be described in more detail with reference to the drawings. It should be understood, however, that the embodiments of the present subject matter are not intended to be limited to the specific forms, but include all modifications, equivalents, and alternatives falling within the spirit and scope of the present subject matter.
FIG. 1 is a block diagram illustrating a system for contextual appearance analysis, in accordance with an embodiment of the present disclosure. With reference to FIG. 1, there is shown a block diagram that includes a system 100 for contextual appearance analysis. The system 100 includes a processor 104 and a memory 106. In an implementation, the system 100 further includes a machine learning model 108. The processor 104 is communicatively coupled with the memory 106 and the machine learning model 108.
In an implementation, the processor 104, the memory 106, and the machine learning model 108 may be implemented on a same server, such as a server 102. In some implementations, the system 100 further includes an appearance standards database 110. In some implementations, the appearance standards database 110 may be stored in the same server, such as the server 102. In some implementations, the appearance standards database 110 is communicatively coupled to the server 102, via a communication network 112. The server 102 may be communicatively coupled to a plurality of user devices, such as a user device 114, via the communication network 112. A mobile application or a web platform may be accessible from the user device 114. The user device 114 includes an input user interface (UI) 116 and an output UI 118.
The present disclosure provides the system 100 for contextual appearance analysis, where the system 100 integrates advanced image processing and machine learning techniques to objectively evaluate adherence to appearance and safety standards across various industries. The system 100 includes the processor 104 that receives a digital image of the subject and contextual information, analyses the subject based on context-specific criteria, and generates an appearance score along with tailored recommendations for improvement. The technology offers several advantages, including the ability to handle diverse and dynamic standards, automate the evaluation process to reduce human bias, and provide precise, context-aware feedback. This enables organizations to ensure compliance with specific requirements efficiently and effectively, while also offering individuals personalized guidance to meet their industry-specific standards.
The server 102 includes suitable logic, circuitry, interfaces, and code that may be configured to communicate with the user device 114 via the communication network 112. In an implementation, the server 102 may be a master server or a master machine that is a part of a data center that controls an array of other cloud servers communicatively coupled to it for load balancing, running customized applications, and efficient data management. Examples of the server 102 may include, but are not limited to a cloud server, an application server, a data server, or an electronic data processing device.
The processor 104 refers to a computational element that is operable to respond to and processes instructions that drive the system 100. The processor 104 may refer to one or more individual processors, processing devices, and various elements associated with a processing device that may be shared by other processing devices. Additionally, the one or more individual processors, processing devices, and elements are arranged in various architectures for responding to and processing the instructions that drive the system 100. In some implementations, the processor 104 may be an independent unit and may be located outside the server 102 of the system 100. Examples of the processor 104 may include but are not limited to, a hardware processor, a digital signal processor (DSP), a microprocessor, a microcontroller, a complex instruction set computing (CISC) processor, an application-specific integrated circuit (ASIC) processor, a reduced instruction set (RISC) processor, a very long instruction word (VLIW) processor, a state machine, a data processing unit, a graphics processing unit (GPU), and other processors or control circuitry.
The memory 106 refers to a volatile or persistent medium, such as an electrical circuit, magnetic disk, virtual memory, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory 106 is a non-volatile mass storage, such as a physical storage media. Furthermore, a single memory may encompass and, in a scenario, and the system 100 is distributed, the processor 104, the memory 106 and/or storage capability may be distributed as well. Examples of implementation of the memory 106 may include, but are not limited to, an Electrically Erasable Programmable Read-Only Memory (EEPROM), Dynamic Random-Access Memory (DRAM), Random Access Memory (RAM), Read-Only Memory (ROM), Hard Disk Drive (HDD), Flash memory, a Secure Digital (SD) card, Solid-State Drive (SSD), and/or CPU cache memory.
The machine learning model 108 refers to an advanced operations trained to analyze and interpret various aspects of the subject's appearance based on context-specific criteria. It processes input data, such as digital images and contextual information, to detect and evaluate parameters relevant to different standards-whether for professional attire, safety equipment, dental alignment, or other industry-specific requirements. By leveraging extensive datasets and context-aware training, the machine learning model 108 enhances the system's ability to provide accurate, objective assessments and personalized recommendations for improvement.
The appearance standards database 110 refers to a comprehensive repository that stores criteria and guidelines for various appearance and safety standards across different industries. The appearance standards database 110 is implemented using a knowledge graph structure, which allows for complex relationships between different appearance contexts and standards to be represented and queried efficiently. The knowledge graph structure enables the system 100 to capture nuanced relationships between various appearance elements, contexts, and standards. For example, the knowledge graphs can represent how certain clothing styles are appropriate in some professional contexts but not in others, or how safety equipment requirements change based on specific industry regulations.
In addition to the knowledge graph, the appearance standards database 110 incorporates Retrieval-Augmented Generation (RAG) models. The RAG models enhance the system's 100 ability to interpret and apply context-specific standards dynamically. When the system 100 needs to determine the relevant standards for a given context, the system 100 uses the RAG models to query the knowledge graph, retrieve relevant information, and generate a comprehensive set of context-specific criteria.
The combination of the knowledge graph and RAG models allows the appearance standards database 110 to be more flexible and adaptive than a traditional relational database. The appearance standards database 110 may handle a wide range of queries and contexts, even those that may not have been explicitly defined in advance. This approach enables the system 100 to provide more nuanced and context-aware evaluations of appearance standards. The appearance standards database 110 includes specific requirements for contexts such as, but not limited to, professional attire, safety equipment compliance, dental alignment, and more. The system 100 accesses this enhanced database to compare the analyzed parameters of the subject against the relevant standards, ensuring that the evaluation is tailored to the specific context. Furthermore, the appearance standards database 110 is regularly updated to reflect current industry practices and standards. The knowledge graph structure facilitates these updates, as new information can be easily integrated into the existing network of relationships. This ensures that the system 100 always provides accurate and up-to-date assessments based on the latest standards and practices.
The communication network 112 includes a medium (e.g., a communication channel) through which the user device 114 communicates with the server 102. The communication network 112 may be a wired or wireless communication network. Examples of the communication network 112 may include, but are not limited to, Internet, a Local Area Network (LAN), a wireless personal area network (WPAN), a Wireless Local Area Network (WLAN), a wireless wide area network (WWAN), a cloud network, a Long-Term Evolution (LTE) network, a plain old telephone service (POTS), a Metropolitan Area Network (MAN), and/or the Internet.
The user device 114 refers to any electronic device used by the end-user to interact with the system. This can include smartphones, tablets, computers, or specialized terminals. The user device 114 serves as the interface through which users can input images and contextual information, receive appearance scores, view discrepancies between analysed parameters and standards, and access recommendations for improvement. It facilitates the entire user experience by providing a seamless connection between the user and the system's analytical capabilities.
The input UI 116 refers to a user interface through which users provide the necessary data to the system 100, including digital images and contextual information. The input UI 116 is designed to be intuitive and user-friendly, allowing users to easily upload images, enter text-based information, or select predefined options related to the context in which the appearance or standard is being analysed. The input UI 116 also guides users in capturing appropriate images and ensures that the data provided aligns with the requirements for accurate analysis by the system 100.
The output UI 118 refers to a user interface through which the results of the appearance analysis are presented to the user. In some implementations, the output UI 118 is a graphical user interface (GUI) displaying the appearance score, the quantified discrepancies, and the recommendations for improvement. The output UI 118 is designed to be clear and informative, allowing users to easily understand the analysis results and take the necessary actions based on the provided feedback. The output UI 118 may also include features such as before-and-after comparisons, visualizations of recommended changes, and links to relevant resources.
In operation, the processor 104 is configured to receive a digital image of a subject and contextual information specifying an appearance context of the subject, via the input UI 116. In some implementations, the contextual information includes context-specific requirements including at least one of: professional attire, event dress codes, fitness goals, dental alignment, and beauty contest criteria. In some other implementations, the context-specific requirements may include, but are not limited to, workplace safety protocols, uniform standards in industrial settings, cultural or religious dress guidelines, personal protective equipment (PPE) compliance, hair and grooming standards, and specific aesthetic preferences for branding or marketing purposes. In some examples, the context-specific requirements are dynamically applied to the appearance analysis, ensuring that the system 100 may be tailored to a wide range of scenarios, from ensuring safety compliance in hazardous environments to meeting specific aesthetic standards in industries such as fashion or entertainment.
Receiving the digital image of the subject and the contextual information specifying the appearance context of the subject enables the system 100 to gather necessary input data for performing a tailored appearance evaluation. The digital image provides the visual information of the subject to be analysed, while the contextual information serves to define the specific parameters and standards against which the appearance will be evaluated. The system 100 employs this approach to ensure that the analysis is relevant and accurate for the given scenario. By accepting both visual and contextual inputs simultaneously, the processor 104 may align its analysis operations and criteria selection with the specific appearance context, whether it be a professional setting, a social event, or a specialized evaluation like dental aesthetics or fitness progress. This dual input mechanism allows for a more nuanced and context-aware analysis compared to systems that might rely solely on image data.
The receipt of the digital image of the subject and the contextual information is technically implemented through the input UI 116, which may include various input methods such as, but not limited to, file uploads, camera integration, or form inputs for contextual details. The processor 104 is configured to interpret and correlate the digital image of the subject with the contextual information, preparing them for subsequent processing steps. This may involve image preprocessing techniques to ensure the digital image is in a suitable format for analysis, as well as parsing and categorizing the contextual information to guide the selection of appropriate evaluation criteria. In some implementations, the processor 104 may be pre-programmed or set up to automatically understand and apply predefined contextual information based on predefined rules or settings. The pre-programming or setting up to automatically understand and apply predefined contextual information may involve setting up default contexts or scenarios that the system 100 recognizes without requiring input from the user. For example, if the system 100 is used in a retail environment, in some cases, the system 100 may be configured to automatically apply the context of "customer service" without needing the user to specify it every time. In some other implementations, the user may manually input the contextual information based on the specific needs or steps of the application they are using. For example, if a user is using the system 100 to analyze their appearance for a job interview, the user may enter "job interview" as the contextual information. In such implementations, Large Language Models (LLMs) and Retrieval-Augmented Generation models (RAGs) may be utilized to process and comprehend the contextual information provided by the users in a natural language. LLMs are advanced models trained on vast amounts of text data, enabling them to understand and generate human-like language. RAGs enhance this capability by retrieving relevant information from external databases or documents, which is then used to generate more accurate and context-specific responses. Together, the LLMs and the RAGs allow the system 100 to interpret user inputs, such as the context for an image, in a way that aligns with the user's intent.
The technical advantage of receiving the digital image of the subject and the contextual information lies in its flexibility and customization capabilities. By accepting context along with the digital image, the processor 104 may dynamically adjust its analysis parameters, eliminating the need for multiple specialized systems for different appearance contexts. By dynamically adjusting the analysis parameters, the processor 104 may enhances the versatility and applicability of the system 100 across various domains. Moreover, by considering the context from beginning, the system 100 may filter out irrelevant criteria and focus on the most pertinent aspects of appearance for the given situation. This targeted analysis not only improves the accuracy of the results but also enhances computational efficiency by prioritizing relevant features and reducing unnecessary processing.
In some implementations, the processor 104 is further configured to: guide a user in capturing the digital image for analysis via the input user interface 116. In such implementations, the guidance may include, but not limited to, real-time feedback on factors such as lighting, framing, and focus to ensure the image meets the necessary quality standards for accurate analysis. Further, in order to guide the user in capturing the digital image for analysis, the processor 104 is configured to analyze initial conditions of the captured digital image and providing on-screen prompts or adjustments through the input UI 116. For instance, if lighting is insufficient or the subject is not properly centered, the processor 104 may alert the user to reposition the camera or adjust the environment. By alerting the user, the processor 104 may ensure that the digital image is optimized for subsequent analysis, enhancing the reliability of the appearance evaluation. Additionally, guiding the user in capturing the digital image streamlines the user experience by reducing the likelihood of needing to recapture images, thereby saving time and improving the overall efficiency of the system 100. By embedding such guidance feature directly while capturing the digital image or uploading the digital image, the processor 104 leverages its computational capabilities to pre-emptively address potential issues that may compromise the quality of the analysis, ensuring that the digital images captured are consistently suitable for the intended contextual analysis.
In some implementations, the processor 104 is further configured to validate the received digital image and contextual information before proceeding with segmentation and analysis. This validation process involves checking the quality and format of the digital image to ensure it is suitable for accurate analysis, such as verifying resolution, clarity, and image format. Additionally, the processor 104 may validate the contextual information by ensuring that it is complete, relevant, and properly formatted for the specific analysis being conducted. By performing the above mentioned validation steps, the system 100 ensures that the data is reliable and meets the necessary criteria, reducing the risk of errors or inaccuracies in the subsequent processing stages.
The processor 104 is further configured to access the appearance standards database 110 containing the criteria for a plurality of appearance contexts including the appearance context of the subject. Accessing the appearance standards database 110 enables the processor 104 to retrieve specific criteria relevant to the appearance context of the subject being analysed. The appearance standards database 110 serves as a comprehensive repository of evaluation criteria for various appearance contexts, ranging from professional dress codes to casual event attire, dental aesthetics, fitness standards, and beyond. The system 100 employs the appearance standards database 110 access mechanism to ensure that the appearance analysis is grounded in appropriate and up-to-date standards for the given context. By maintaining the appearance standards database 110 of appearance criteria, the system 100 may adapt to different scenarios without requiring significant reconfiguration or separate specialized systems for each context. The processor 104 queries the appearance standards database 110 using the contextual information provided at beginning, retrieving the specific set of criteria that aligns with the subject's appearance context.
The processor 104 is configured to access the appearance standards database 110, which utilizes the knowledge graph structure and RAG models. When retrieving context-specific standards, the processor 104 formulates queries based on the provided contextual information. These queries are processed by the RAG models, which interact with the knowledge graph to retrieve relevant information and generate a comprehensive set of context-specific standards. This approach allows the processor 104 to handle complex and nuanced queries about appearance standards. For example, if the context involves a formal business event in a specific cultural setting, the RAG models can combine information about formal business attire with cultural-specific expectations to generate a more accurate and relevant set of standards. The use of knowledge graphs and RAG models in the appearance standards database 110 enhances the system's ability to provide context-aware and flexible evaluations without requiring changes to the core functionality of the processor 104 or other system components
During the analysis of the subject's appearance, the processor 104 leverages the enhanced capabilities of the appearance standards database 110. The appearance standards database 110 uses the provided contextual information to formulate queries that are processed by the RAG models. The RAG models interact with the knowledge graph to retrieve and synthesize relevant appearance standards. This dynamic approach allows the system 100 to handle complex and nuanced appearance contexts that may not be fully captured by predefined rules alone. The resulting context-specific standards are then used in the comparison and evaluation process, ensuring that the analysis is highly relevant and adaptable to various scenarios.
The integration of knowledge graphs and Retrieval-Augmented Generation models in the appearance standards database 110 allows for more flexible and adaptive standard determination, capable of handling a wider range of contexts and evolving standards. The system 100 may now process complex contextual information more effectively, leading to more accurate and nuanced appearance analyses. Additionally, this approach facilitates easier updates to the appearance standards, as new information can be incorporated into the knowledge graph without requiring a complete overhaul of the system. These enhancements significantly improve the system's ability to provide relevant and up-to-date appearance evaluations across various industries and contexts.
The processor 104 is further configured to segment the subject from a background in the digital image. In some examples, the segmentation of the subject from the background in the digital image is achieved using advanced image processing techniques, such as, but not limited to, convolutional neural networks (CNNs) specifically trained for image segmentation tasks. By effectively distinguishing the subject from the background, the processor 104 may eliminate any extraneous visual data that may otherwise interfere with the accuracy of the appearance analysis. This is particularly important in diverse environments where background elements may vary significantly and potentially skew the results. The segmentation of the subject from the background in the digital image ensures that the analysis focuses solely on the subject, enabling precise evaluation of appearance factors such as clothing, accessories, or physical features. By doing so, the system 100 enhances both the accuracy and reliability of the analysis, making it applicable across various contexts and environments.
The processor 104 is further configured to detect and analyze a plurality of parameters associated with the subject in the digital image based on the contextual information. In some implementations, the plurality of parameters includes at least one of: clothing, accessories, physical appearance, dental aesthetics, and body composition of the subject. In some other implementations, the plurality of parameters may include, but are not limited to, factors such as hairstyle, skin condition, posture, or the presence of safety equipment. Advanced image processing and computer vision techniques may be employed to identify and assess the plurality of parameters.
In some examples, following segmentation of the subject from the digital image, an object detection process is performed using a CNN-based object detection model. The object detection process facilitates identification of accessories that the subject may be wearing or carrying, which may be relevant to certain appearance contexts. The object detection process allows the system 100 to recognize and evaluate items such as jewellery, bags, or profession-specific tools that may be integral to the overall appearance assessment. Further, clothing analysis is performed that involves further segmentation to isolate clothing items, followed by a detailed analysis of various attributes including pattern, style, color, texture, and coverage. The processor 104 employs a combination of image processing techniques and deep learning models to extract these features, providing a comprehensive evaluation of the subject's attire. Furthermore, the processor 104 is further configured to perform hair analysis through a two-step process. First, hair segmentation is performed using a CNN-based image segmentation model to isolate the hair region. This is followed by hairstyle analysis, which classifies the hairstyle based on the provided context. This is particularly relevant for industries or scenarios where specific hairstyles are required or preferred. Moreover, to provide a holistic understanding of the image, the system 100 also incorporates image captioning using a retrieval augmented captioning schema. Incorporating image captioning aids in generating more comprehensive and context-aware recommendations by providing a textual description of the overall appearance.
By breaking down the image into various components and analyzing each with specialized techniques, the system 100 may capture subtle details that might be missed by more generalized approaches. The use of advanced machine learning models, particularly CNNs, allows for high accuracy in feature detection and classification.
In some implementations, the processor 104 is further configured to: perform facial analysis of the subject to assess features and adornments of the subject based on the context-specific standards. Beginning with the extraction of facial landmarks, a CNN model, trained on a specialized dataset, is used to precisely predict key facial points. The extracted facial landmarks serve as the foundation for further facial analysis, which includes deep facial skin analysis, face shape analysis, makeup analysis, age prediction, and beard analysis when applicable. The specific analyses performed are context-dependent, ensuring that only relevant facial features are evaluated for the given appearance scenario.
The processor 104 is further configured to compare the analysed parameters with context-specific standards received from the appearance standards database. In some implementations, once the context specific standards are retrieved, the processor 100 compares the context specific standards with the analysed parameters. For example, in the context of a business meeting, the standards may specify formal attire like a suit and tie. The processor 104 may analyze the subject's clothing in the image to determine whether it matches these requirements by examining attributes such as color, style, and coverage. In another context, such as a dental alignment check, the processor 104 is configured to retrieve standards related to ideal tooth alignment and compares these with the subject's dental features detected in the digital image, such as the position and spacing of the teeth. Any discrepancies between the detected alignment and the context specific standard are identified by the processor 104.
The processor 104 is further configured to generate an appearance score by quantifying discrepancies between the analysed parameters and the context-specific standards, and generate recommendations for improvement based on the comparison. This functionality transforms the raw data from the image analysis and context-specific standards comparison into actionable insights and quantifiable metrics. The appearance score, a numerical or categorical representation of how well the subject's appearance aligns with the context-specific standards, serves as a quick reference point for overall compliance. The appearance score is typically prominently displayed, allowing the users to immediately gauge their current status. Further, the appearance score is computed by quantifying the discrepancies between the analysed parameters of the subject and the context-specific standards retrieved from the appearance standards database 110. This quantification process involves complex operations that weigh various factors based on their importance in the given context, resulting in a comprehensive numerical representation of the subject's appearance compliance. The system 100 employs such scoring mechanism to provide an objective measure of how well the subject's appearance aligns with the context-specific standards. By combining multiple analysed parameters into a single score, the system 100 offers a clear and easily understandable assessment of overall appearance compliance. The appearance score serves as a quick reference point for users to gauge their current status and track improvements over time.
In conjunction with the appearance score, the processor 104 generates tailored recommendations for improvement. The generated recommendations are derived from the identified discrepancies between the analysed parameters and the context-specific standards. The processor 104 utilizes advanced decision-making operations to prioritize and formulate suggestions that may have the most significant impact on improving the appearance score. This may involve suggesting changes to specific aspects of appearance, such as clothing choices, grooming techniques, or posture adjustments, depending on the context and the most prominent discrepancies identified.
Technically, such feature is implemented through a combination of statistical analysis, machine learning operations, and rule-based systems. The processor 104 employs sophisticated mathematical models to calculate the appearance score, potentially using weighted averages, multi-factor analysis, or more advanced AI-driven scoring mechanisms. In some examples, the generation of the recommendation involves natural language processing techniques to convert technical discrepancies into clear, actionable suggestions. The technical advantage of generating the recommendation lies in its ability to provide both quantitative and qualitative feedback in a single, comprehensive analysis. The appearance score offers a measurable benchmark for improvement, while the recommendations provide a clear pathway to achieve that improvement. Both the generation of the appearance score and the recommendations enhances utility of the system 100 across various applications, from personal grooming assistance to professional image consulting. Furthermore, by basing both the appearance score and the recommendations on context-specific standards and individual analysis, the system 100 provides highly relevant and tailored guidance. This personalization can significantly enhance the effectiveness of appearance improvements compared to generic advice or subjective assessments.
The generation of the appearance score and recommendations also contributes to objectivity and consistency of the system 100. By relying on quantifiable metrics and standardized criteria, the system 100 reduces the influence of personal biases or inconsistencies that may occur in human-led appearance assessments. This objectivity is particularly valuable in professional contexts where fair and standardized evaluation is crucial.
In essence, ability of the processor 104 to generate the appearance score and tailored recommendations represents a powerful analytical tool in the field of appearance assessment. The generation of the appearance sore and the recommendations bridge a gap between complex image analysis and practical, actionable insights, marking a significant advancement in automated appearance evaluation technology. The generation of the appearance sore and the recommendations not only enhances utility of the system 100 but also opens up new possibilities for applications in various fields, from personal styling to professional development and beyond.
In some implementations, to analyze the parameters and quantify discrepancies for generating the appearance score, the processor 104 is configured to utilize at least one machine learning model 108 trained on context-specific datasets. The process begins with the training of machine learning models, typically deep neural networks, on carefully curated training datasets that correspond to different appearance contexts. The training datasets may include images of professional attire for various industries, casual wear for different social settings, or specialized imagery for contexts like fitness evaluation or fashion competitions. By training on the context-specific datasets, the machine learning models 108 learn to recognize and evaluate the nuances of appearance that are particularly relevant to each scenario.
In some examples, when analyzing a subject's appearance, the processor 104 selects and applies the appropriate machine learning model 108 based on the given context. The machine learning model 108 processes the input image, extracting relevant features and comparing them against the learned standards. Architecture of the machine learning model 108 architecture allows it to simultaneously consider multiple aspects of appearance, such as clothing style, color coordination, accessory choices, and grooming, weighing each factor according to its importance in the specific context.
The quantification of discrepancies is achieved through the ability of machine learning model 108 to generate numerical representations of various appearance aspects. The numerical representations of various appearance aspects are compared against the ideal or expected values for the given context, with the differences calculated and weighted to produce a comprehensive appearance score. This approach allows for a nuanced evaluation that can capture subtle variations in appearance quality.
In a first example, utilizing the at least one machine learning model 108 provides a high degree of adaptability, as models can be retrained or fine-tuned to accommodate evolving appearance standards or new contexts without requiring a complete system overhaul. In a second example, the machine learning models 108 may identify complex patterns and relationships in appearance data that might not be immediately apparent to human observers or rule-based systems.
In some implementations, the recommendations for improvement include computer-generated images of the subject with suggested modifications. In such implementation, by using advanced image processing and machine learning techniques, the processor 104 is configured to create realistic renderings of the subject with the proposed changes applied. For example, if the processor 104 recommends a different hairstyle or makeup look, the processor 104 may generate an image of the subject featuring those adjustments. The visualizations allow the user to see how the subject may look with the recommended changes, providing a clear and tangible understanding of the suggestions. Generating the computer-generated images of the subject with suggested modifications may enhance the effectiveness of the recommendations by offering a personalized and visual preview, making it easier for users to evaluate and consider implementing the advice.
In some implementations, the processor 104 is further configured to utilize an artificial intelligence (AI) engine for generating the recommendations for improvement. The artificial intelligence engine processes the analyzed parameters, context-specific standards, and other relevant data to generate personalized recommendations. This AI-driven approach allows for more dynamic and adaptive recommendation generation, potentially improving the relevance and effectiveness of the suggestions across various contexts and individual cases. The specific implementation and functioning of the artificial intelligence engine may vary and can be tailored to the particular needs of the appearance analysis system 100.
In some examples, the system 100 incorporates a specialized artificial intelligence (AI) engine focused on beauty and appearance analysis. The specialized AI engine is designed to provide detailed recommendations specifically for beauty-related aspects of a person's appearance. The beauty-focused AI engine utilizes advanced image processing and machine learning techniques to analyze various beauty-related parameters of the subject. These parameters may include, but are not limited to, facial features, skin condition, makeup application, and overall aesthetic harmony.
For example, when analyzing a subject's facial appearance, the beauty-focused AI engine may assess skin tone and texture, providing recommendations for skincare routines or products that could improve overall skin health and appearance, analyze current makeup application, if any, and suggest improvements or alternatives that might better suit the subject's features and the given context, evaluate facial symmetry and propose hairstyles or accessory choices that could enhance the subject's overall appearance, consider the subject's unique facial features and suggest personalized beauty techniques that could highlight their best attributes.
The beauty-focused AI engine's recommendations are context-aware, taking into account the specific appearance context provided (e.g., professional environment, social event, etc.). This ensures that the beauty-related suggestions are not only aesthetically pleasing but also appropriate for the given situation. Furthermore, the beauty-focused AI engine can generate visual representations of its recommendations. For instance, the beauty-focused AI engine may provide digitally altered images showing how the subject might look with the suggested changes applied. This visual feedback may help users better understand and evaluate the potential impact of the recommendations. It's important to note that while the beauty-focused AI engine focuses on beauty-related aspects, the beauty-focused AI engine operates within the broader context of the appearance analysis system 100. Its recommendations are integrated with other appearance factors to provide a comprehensive and balanced evaluation of the subject's overall appearance in relation to the specified context. The incorporation of the specialized beauty-focused AI engine enhances the system's capability to provide nuanced and personalized recommendations in the realm of aesthetic appearance. This feature is particularly valuable in contexts where detailed beauty analysis is crucial, such as in the fashion industry, personal styling, or professional image consulting. By combining this specialized analysis with the broader appearance evaluation, the system 100 offers a more comprehensive and tailored approach to appearance improvement.
In some implementations, the recommendations are customized based on available resources specified in the appearance standards database. The customization of recommendations based on available resources specified in the appearance standards database enhances the practicality of the system's suggestions. This feature works by cross-referencing the identified discrepancies with a database of approved or available resources. For example, if the system 100 identifies that a subject's attire doesn't meet the required professional standards for a specific workplace, it will only recommend items that are explicitly approved or available within the organization. If the appearance standards database 110 specifies that navy blue blazers from Brand X are an approved item, the recommendation might suggest "Consider wearing a navy blue blazer from Brand X to improve your professional appearance score."
In some implementations, the processor 104 is further configured to: generate before-and-after comparisons to visualize recommended changes. In such implementations, the generation of before-and-after comparisons to visualize recommended changes is achieved through advanced image processing and computer graphics techniques. The processor 104 uses the original image of the subject as the "before" image. For the "after" image, the processor 104 applies digital alterations based on the recommendations. Application of digital alterations based on the recommendations may involve techniques such as, but limited to, digital clothing overlay, hair style modification, or subtle adjustments to posture or grooming. For instance, if the recommendation is to wear a tie, the system 100 could generate an "after" image showing the subject with an appropriate tie digitally added to their existing outfit.
The processor 104 is further configured to control display, via the output user interface 118, to provide the appearance score, quantified discrepancies between the analysed parameters and the context-specific standards, and the recommendations for achieving compliance with the context-specific standards. Specifically, the output UI 118 is designed to convey three key elements: the appearance score, quantified discrepancies, and recommendations for achieving compliance. By controlling display of the three key elements, the processor 104 ensures that the complex data generated by the system 100 is presented in a clear, understandable, and actionable format to the end-user. The technical implementation of this display control involves sophisticated user interface design and data visualization techniques. The processor 104 may translate complex analytical data into visually appealing and easily digestible formats that may involve the use of charts, graphs, color-coding, or interactive elements, allowing the users to explore different aspects of the analysis in more detail. By presenting the analysis results in a clear and structured manner, the system 100 empowers users to understand their current appearance status and take specific steps for improvement. Furthermore, user engagement and system effectiveness may also be enhanced. The clear presentation of scores, discrepancies, and recommendations encourages users to interact more deeply with the analysis results, potentially leading to better compliance with appearance standards over time.
In some implementations, the processor 104 is further configured to: track and analyze appearance scores over time for the subject or groups of the subject. In such implementation, tracking and analyzing appearance scores over time for the subject or groups involves maintaining a database of historical scores and implementing trend analysis algorithms. In some examples, each time an appearance analysis is conducted, the appearance score is saved along with relevant metadata such as date, time, and context. In such examples, the processor 104 may then generate graphs or charts showing how an individual's or group's scores have changed over time. The track and analyze appearance scores may be particularly useful in professional settings, allowing managers to track improvements in team appearance standards over weeks or months.
In some implementations, the processor 104 is further configured to: store the appearance score and associated data for historical tracking and trend analysis. In such implementations, storing the appearance score and associated data for historical tracking and trend analysis involves creating a comprehensive data storage and retrieval system. Each analysis generates not just a score, but also detailed data about various parameters analysed, recommendations given, and the context of the analysis. This data is stored in a structured format, likely using a database system that allows for efficient querying and analysis. The system 100 may store data points such as date and time of analysis, appearance score, scores for individual parameters (e.g., clothing score, grooming score), context of the analysis (e.g., workplace, social event), recommendations provided, and whether recommendations are implemented (if follow-up analyses are done). The stored data may then be used to generate detailed reports on trends and patterns. For example, the system 100 may identify that a particular subject consistently scores low on grooming during winter months, or that group scores tend to improve significantly after company-wide training sessions on appearance standards.
FIGs. 2A-2D are schematic diagrams illustrating a series of operations for contextual appearance analysis, in accordance with an embodiment of the present disclosure. FIGs. 2A-2D are described in conjunction with elements from FIG. 1. With reference to FIG. 2A, there is shown a schematic diagram of the user device 114 illustrating the input UI 116. In the illustrated embodiment of FIG. 2A, the input UI 116 includes a first button 202 that is used for inputting one or more digital images of a subject and a second button 204 that is used for inputting one or more digital images of a group of subjects. Further, the input UI 116 may also have option to input contextual information. After that, inputted data such as the digital image and the contextual information may be analysed based on various different analysis models. With reference to FIG. 2B, there is shown a schematic diagram of the user device 114 illustrating the input UI 116. In the illustrated embodiment of FIG. 2B, the input UI 116 includes a plurality of analysis options. The plurality of analysis options may include a first analysis option 206A, a second analysis option, and so on up to an Nth analysis option 206N. The plurality of analysis may include, but not limited to, hair analysis, facial analysis, clothing analysis, dental alignment analysis, safety equipment analysis and the like. In some examples, one or more analysis options from the plurality of analysis options 206A to 206N may be selected as per application requirements. With reference to FIG. 2C, there is shown a schematic diagram of the user device 114 illustrating the output UI 118. In the illustrated embodiment of FIG. 2C, the output UI 118 includes a numerical value 208 representing the appearance score as a percentage. Further, the output UI 118 includes a graphical representation 210 corresponding to the numerical value 208, with a portion of the circle filled to illustrate the appearance score graphically. Based on the appearance score, the processor 104 generates recommendations for improving the current appearance score. With reference to FIG. 2D, there is shown a schematic diagram of the user device 114 illustrating the output UI 118. In the illustrated embodiment of FIG. 2D, the output UI 118 includes a heading section 212 for mentioning a particular parameter for which details are provided below the heading section 212. In this example, the particular parameter is hair styling. However, in some other examples, the particular parameter may be clothing, accessories, physical appearance, dental aesthetics, and body composition of the subject. The output UI 118 further includes a current status section 214 for mentioning a current status of the particular parameter. The output UI 118 further includes a recommendation section 216 for mentioning the recommendation for improvement such that the subject may improve the appearance score and the current status by following the recommendations.
FIG. 3 is a flowchart of a method for contextual appearance analysis, in accordance with an embodiment of the present disclosure. FIG. 3 is explained in conjunction with elements from FIGs. 1 to 2D. With reference to FIG. 3, there is shown a flowchart of a method 300. The method 300 is executed at the server 102 (of FIG. 1). The method 300 may include steps 302 to 314.
At step 302, the method 300 includes receiving, by the at least one processor 104, the digital image of the subject and the contextual information specifying the appearance context, via the input user interface 116. By accepting context along with the digital image, the processor 104 may dynamically adjust its analysis parameters, eliminating the need for multiple specialized systems for different appearance contexts. By dynamically adjusting the analysis parameters, the processor 104 may enhances the versatility and applicability of the system 100 across various domains.
At step 304, the method 300 further includes accessing, by the at least one processor 104, the appearance standards database 110 containing criteria for the plurality of appearance contexts. As appearance standards evolve or new contexts are introduced, the system 100 may be updated by simply modifying contents of the appearance standards database 110, without requiring changes to the analysis operations. This separation of data (appearance standards) from logic (analysis operation) enhances flexibility and longevity of the system 100.
At step 306, the method 300 further includes segmenting, by the at least one processor 104, the subject from the background in the digital image. The segmentation of the subject from the background in the digital image ensures that the analysis focuses solely on the subject, enabling precise evaluation of appearance factors such as clothing, accessories, or physical features. By doing so, the system 100 enhances both the accuracy and reliability of the analysis, making it applicable across various contexts and environments.
At step 308, the method 300 further includes detecting and analyzing, by the at least one processor 104, the plurality of parameters associated with the subject in the digital image based on the contextual information. By breaking down the digital image into various components and analyzing each with specialized techniques, the system 100 may capture subtle details that might be missed by more generalized approaches. The use of advanced machine learning models, particularly CNNs, allows for high accuracy in feature detection and classification.
At step 310, the method 300 further includes comparing, by the at least one processor 104, the analysed parameters with context-specific standards received from the appearance standards database. Any discrepancies between the analysed parameters and the context specific standard are identified by the processor 104.
At step 312, the method 300 further includes generating, by the at least one processor 104, the appearance score by quantifying discrepancies between the analysed parameters and the context-specific standards. The appearance score provides an objective measure of how well the subject's appearance aligns with the context-specific standards. By combining multiple analysed parameters into a single score, the system 100 offers a clear and easily understandable assessment of overall appearance compliance. The appearance score serves as a quick reference point for users to gauge their current status and track improvements over time. Further, at step 312, the method 300 further includes generating, by the at least one processor 104, the recommendations for improvement based on the comparison. The processor 104 utilizes advanced decision-making operations to prioritize and formulate suggestions that may have the most significant impact on improving the appearance score. This may involve suggesting changes to specific aspects of appearance.
At step 314, the method 300 further includes controlling, by the at least one processor 104, display to provide the appearance score, the quantified discrepancies between the analysed parameters and the context-specific standards, and the recommendations for achieving compliance with the context-specific standards, via the output user interface 118. By presenting the analysis results in a clear and structured manner, the processor 104 empowers the users to understand their current appearance status and take specific steps for improvement.
Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non-exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural. The word "exemplary" is used herein to mean "serving as an example, instance or illustration". Any embodiment described as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments and/or to exclude the incorporation of features from other embodiments. The word "optionally" is used herein to mean "is provided in some embodiments and not provided in other embodiments". It is appreciated that certain features of the present disclosure, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the present disclosure, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable combination or as suitable in any other described embodiment of the disclosure. 
, Claims:CLAIMS
We Claim:
1. A system (100) for contextual appearance analysis, the system (100) comprising at least one processor (104) configured to:
receive a digital image of a subject and contextual information specifying an appearance context of the subject, via an input user interface (116);
access an appearance standards database (110) containing criteria for a plurality of appearance contexts comprising the appearance context of the subject;
segment the subject from a background in the digital image;
detect and analyze a plurality of parameters associated with the subject in the digital image based on the contextual information;
compare the analysed parameters with context-specific standards received from the appearance standards database (110);
generate an appearance score by quantifying discrepancies between the analysed parameters and the context-specific standards, and generate recommendations for improvement based on the comparison; and
control display, via an output user interface (118), to provide the appearance score, quantified discrepancies between the analysed parameters and the context-specific standards, and the recommendations for achieving compliance with the context-specific standards.
2. The system (100) as claimed in claim 1, wherein the plurality of parameters comprises at least one of: clothing, accessories, physical appearance, dental aesthetics, and body composition of the subject.
3. The system (100) as claimed in claim 1, wherein the processor (104) is further configured to: guide a user in capturing the digital image for analysis via the input user interface (116).
4. The system (100) as claimed in claim 1, wherein the processor (104) is further configured to: perform facial analysis of the subject to assess features and adornments of the subject based on the context-specific standards.
5. The system (100) as claimed in claim 1, wherein the contextual information includes context-specific requirements comprising at least one of: professional attire, event dress codes, fitness goals, dental alignment, and beauty contest criteria.
6. The system (100) as claimed in claim 1, wherein, to analyze the parameters and quantify discrepancies for generating the appearance score, the processor (104) is configured to utilize at least one machine learning model (108) trained on context-specific datasets.
7. The system (100) as claimed in claim 1, wherein the recommendations for improvement include computer-generated images of the subject with suggested modifications.
8. The system (100) as claimed in claim 1, wherein the processor (104) is further configured to: generate before-and-after comparisons to visualize recommended changes.
9. The system (100) as claimed in claim 1, wherein the processor (104) is further configured to: track and analyze appearance scores over time for the subject or groups of the subject.
10. The system (100) as claimed in claim 1, wherein the recommendations are customized based on available resources specified in the appearance standards database (110).
11. The system (100) as claimed in claim 1, wherein the output user interface (118) is a graphical user interface displaying the appearance score, the quantified discrepancies, and the recommendations for improvement.
12. The system (100) as claimed in claim 1, wherein the processor (104) is further configured to: validate the received digital image and the contextual information prior to performing the segmentation and analysis.
13. The system (100) as claimed in claim 1, wherein the processor (104) is further configured to: store the appearance score and associated data for historical tracking and trend analysis.
14. The system (100) as claimed in claim 1, wherein the processor (104) is further configured to utilize an artificial intelligence engine for generating the recommendations for improvement.
15. A method (300) for contextual appearance analysis, the method (300) comprising:
receiving, by at least one processor (104), a digital image of a subject and contextual information specifying an appearance context, via an input user interface (116);
accessing, by the at least one processor (104), an appearance standards database (110) containing criteria for a plurality of appearance contexts;
segmenting, by the at least one processor (104), the subject from a background in the digital image;
detecting and analyzing, by the at least one processor (104), a plurality of parameters associated with the subject in the digital image based on the contextual information;
comparing, by the at least one processor (104), the analysed parameters with context-specific standards received from the appearance standards database (110);
generating, by the at least one processor (104), an appearance score by quantifying discrepancies between the analysed parameters and the context-specific standards, and generating, by the at least one processor (104), recommendations for improvement based on the comparison; and
controlling, by the at least one processor (104), display to provide the appearance score, the quantified discrepancies between the analysed parameters and the context-specific standards, and the recommendations for achieving compliance with the context-specific standards, via an output user interface (118).

Documents