"Thermal Imaging Based Employee Attendance Monitoring System Using Convolutional Neural Networks"

Abstract

The invention is a Thermal Imaging Based Employee Attendance Monitoring System that uses thermal imaging technology and Convolutional Neural Networks (CNNs) to track employee attendance accurately and reliably in real-time, even in challenging environmental conditions such as low light, fog, or rain. Thermal cameras strategically positioned at entry and exit points capture the unique thermal signatures of individuals. These images are processed by CNN models trained on thermal datasets to identify employees based on their thermal patterns, ensuring robust attendance tracking when facial recognition or other visual-based methods may fail. The system integrates cost-effective Raspberry Pi hardware for on-site processing, providing a scalable solution that is easily deployed in academic and industrial settings. With features including real-time monitoring, automated attendance reporting, and environmental adaptability, the invention enhances accuracy, security, and efficiency in attendance management, while protecting employee privacy through the use of non-identifying thermal imaging.

Specification

Description:
Field of the Invention:
[001] The present invention relates to systems and methods for monitoring employee attendance through thermal imaging technology combined with machine learning algorithms, particularly Convolutional Neural Networks (CNNs). This system is designed to capture and analyze thermal images to identify and verify employee presence in real-time, providing a reliable solution for attendance tracking even under challenging environmental conditions such as low light, fog, or rain. By integrating thermal cameras with CNN models deployed on Raspberry Pi hardware, the invention enhances the accuracy and robustness of attendance monitoring in both academic and industrial environments, where traditional visual recognition techniques may be unreliable. This approach aims to improve attendance management processes by leveraging the unique advantages of thermal imaging in scenarios with compromised facial visibility.
Background of the invention and related prior art:
[002] In traditional attendance monitoring systems, visual recognition technologies such as facial recognition and RFID-based methods are commonly employed to identify and track employees. However, these methods often face limitations in environments with poor visibility, such as low-light conditions, fog, or rain, where facial features and visual markers are obscured. Additionally, visual-based recognition systems can struggle to ensure accurate identification when employees are partially masked or obscured, leading to potential errors in attendance recording. Thermal imaging offers a viable alternative, as it captures heat signatures that are unaffected by visibility challenges, providing a more consistent and reliable approach to attendance monitoring. Recent advancements in machine learning, specifically Convolutional Neural Networks (CNNs), have enabled the development of models capable of analyzing thermal images to recognize individuals based on unique thermal patterns. Leveraging these technologies, the present invention introduces a robust and scalable system that uses CNNs and thermal cameras to accurately monitor employee attendance in diverse environmental conditions, addressing limitations of conventional methods and enhancing efficiency in attendance management.
[003] A patent document CN114554153A relates to a video stream transmission control method for a monitoring system and the monitoring system, which comprises the steps of collecting video stream data and environment information data in a monitoring area, and transmitting the environment information data to a monitoring terminal; analyzing the video stream data and the environmental information data to detect whether the state of the monitoring area is abnormal or not; when the monitoring area is abnormal, the video stream segment corresponding to the abnormal moment is intercepted, notification information is generated and transmitted to the monitoring terminal, and whether the video stream segment is checked or not is determined by a worker. The method is used as a practical application of edge calculation in the monitoring field, and divides data transmitted to a monitoring terminal into a digital alarm stream and a video stream, wherein the digital alarm stream comprises: the environmental information data is transmitted when the state is normal, and the notification information is transmitted when the state is abnormal and used for alarming; the video stream refers to a video stream segment corresponding to the abnormal state, and the requirement on the network bandwidth can be obviously reduced compared with the transmission mode of the main code stream and the sub code stream.
[004] Another patent document US6757693B2 disclosed a system and method of data transmission/reception in which picture signals are encoded into image data, the attribute information of the image data is obtained, meta data are generated from the attribute information of the image data. The image data and the meta data are transmitted separately. With this system and method of data transmission/reception, when predetermined conditions are satisfied, i.e. only when a moving body is detected or abnormal data or data including significant information are detected, image data and meta data are transmitted. Therefore, since not all the data are transmitted, the amount of transmitted data is reduced. As a result, the amount of data accumulated in data recipient can be reduced and the load of data analysis operation can be alleviated. In addition, the burden on an operator visually monitoring image data can be eased. Furthermore, inadvertent failure in checking abnormal data or data including significant information can be prevented.
[005] A document CN113362951A discloses a human body infrared thermal structure attendance and health assessment and epidemic prevention early warning system and a method, wherein the human body infrared thermal structure attendance and health assessment and epidemic prevention early warning system is characterized in that: the system comprises a cloud platform, infrared thermal imaging image acquisition equipment and a user terminal; the cloud platform comprises an information recording module, an identity comparison module, an attendance checking module, a health evaluation module, a health report generation module, an epidemic prevention early warning module and a data sending module; the attendance and health assessment and epidemic prevention early warning system and method not only can realize user attendance, but also can accurately and timely reflect the health condition of the user and provide epidemic prevention early warning for the user.
[006] Another document US20190192010A1 discloses methods for determining change in physical condition or illness of a mammal by obtaining a thermal image of the subject and determining the body temperature of the mammal based on the thermal image. An example method is implemented on a first electronic device having a first display and a thermal imaging hardware. The method includes obtaining the thermal image of a mammal using the first electronic device; comparing said thermal image to a reference thermal image of the subject at healthy state with no symptoms or characteristics of the physical condition or illness such as by way of example, flu, fever, hypothermia, ovulation, heat stress, cardiac condition; comparing the intensity of said thermal image to the reference thermal image; and in response to such comparison, determining whether the subject shows symptoms or hallmarks of a certain illness or condition marked by a change in body temperature and associated intensity of the thermal image; and displaying information regarding said determination on the first display of the first electronic device. The method includes first obtaining a reference thermal image of a mammal in a "normal" and/or "healthy" condition, then later obtaining additional image(s) for comparison to the reference image; and in response to such comparison, determining whether the subject shows symptoms of a certain illness or condition marked by a change in body temperature and associated intensity of the thermal image.
[007] A patent document CN108364682A discloses a kind of infrared thermal imaging check-up equipment, cloud server, terminal and systems, wherein infrared thermal imaging check-up equipment includes：First identifier module starts Order receiver module, infrared thermal imagery acquisition module, data transmission blocks. Equipment provided by the invention can be placed in the public places position such as factory, community, rural area, office building, pharmacy, market, subway platform, and user is made to realize unattended self-service formula infrared thermal imagery inspection in 24 hours whenever and wherever possible. Make user eliminate hospital to register, be lined up inspection, the process for waiting for sit-in doctor to check, it can be achieved that complete self-service inspection and health state evaluation. It is easy to operate, use occasion is more flexible, practicability is stronger.
[008] None of these above patents, however alone or in combination, disclose the present invention. The invention consists of certain novel features and a combination of parts hereinafter fully described, illustrated in the accompanying drawings, and particularly pointed out in the appended claims, it being understood that various changes in the details may be made without departing from the spirit, or sacrificing any of the advantages of the present invention.
Summary of the invention:
[009] The invention presents a Thermal Imaging Based Employee Attendance Monitoring System that utilizes thermal imaging and Convolutional Neural Networks (CNNs) to accurately track employee attendance in real-time, even in challenging environments where traditional visual recognition systems may fail. Thermal cameras strategically placed within premises capture thermal images of employees, which are then processed by CNN models trained to recognize individuals based on their unique thermal signatures. By leveraging the power of CNNs with frameworks like TensorFlow, and integrating with cost-effective hardware such as Raspberry Pi for real-time processing, the system ensures reliable performance in diverse environmental conditions, including low-light, fog, or rain. This innovative approach is particularly suited for academic and industrial settings, providing a scalable, automated solution for attendance management that minimizes manual effort, enhances accuracy, and maintains robustness across various conditions.
Detailed description of the invention with accompanying drawings:
[010] For the purpose of facilitating an understanding of the invention, there is illustrated in the accompanying drawing a preferred embodiment thereof, from an inspection of which, when considered in connection with the following description, the invention, its preparation, and many of its advantages should be readily understood and appreciated.
[011] The principal object of the invention is to develop thermal imaging-based employee attendance monitoring system using convolutional neural networks. The Thermal Imaging Based Employee Attendance Monitoring System is designed to provide reliable attendance tracking through a combination of thermal imaging technology and Convolutional Neural Networks (CNNs), ensuring accuracy and adaptability in diverse environmental conditions. Below is a detailed description of the system's components and functions:
1. Thermal Imaging Data Acquisition:
Thermal cameras are deployed at key entry and exit points within a facility, capturing thermal images of employees as they enter and leave. These cameras are positioned strategically to cover all potential access points, minimizing blind spots and ensuring complete coverage for attendance monitoring. The use of thermal imaging enables the system to capture unique heat signatures, which are unaffected by environmental factors such as lighting, fog, or rain.
2. CNN Model Development and Training:
The core of the system is based on CNNs, a type of machine learning model particularly effective in processing image data. Using frameworks like TensorFlow or PyTorch, CNN models are developed and optimized specifically for recognizing thermal images. The models are trained on a large dataset of thermal images that represents the unique thermal patterns of employees, allowing them to accurately distinguish between individuals based on these patterns. Through training, the CNN models learn to identify key features in thermal signatures, achieving high accuracy in recognizing and classifying employees across different environmental conditions.
3. Integration with Raspberry Pi and Thermal Cameras:
Once trained, the CNN models are integrated with Raspberry Pi hardware, which allows for localized, real-time processing of thermal images captured by the thermal cameras. The Raspberry Pi serves as a compact and cost-effective computing unit, capable of running the CNN model for continuous attendance monitoring. Each Raspberry Pi is connected to one or more thermal cameras, enabling data acquisition and processing directly on-site without the need for a central server, thus reducing latency and improving system responsiveness.
4. Real-Time Attendance Monitoring and Reporting:
A centralized interface or dashboard displays real-time attendance data and provides insights into attendance trends, employee presence, and timestamps. This monitoring platform allows administrators to track attendance in real-time and access detailed, automated reports generated based on the processed thermal image data. Reports are customizable and can be exported to various formats for integration with existing HR or payroll systems, making attendance management more efficient and reducing manual errors.
5. Environmental Adaptability and Reliability:
The system is designed to function reliably in a wide range of environmental conditions, including low light, rain, fog, or other situations where visual recognition methods may struggle. Performance testing is conducted across various environmental scenarios to ensure the CNN models maintain accuracy and robustness in recognizing thermal signatures regardless of external factors. This adaptability ensures consistent performance, allowing the system to operate effectively in both indoor and outdoor settings without the need for additional lighting or environmental controls.
6. Security and Privacy:
The use of thermal imaging enhances privacy compared to traditional video-based systems, as thermal images capture heat signatures rather than detailed facial features.
Additionally, the system incorporates security protocols for data encryption and access control, ensuring that sensitive attendance data is securely managed and accessible only to authorized personnel. By focusing on heat signatures rather than identifiable facial images, the system provides a privacy-conscious solution suitable for compliance with data protection regulations.
[012] In summary, this Thermal Imaging Based Employee Attendance Monitoring System combines thermal cameras, CNN-based image processing, and Raspberry Pi hardware to deliver a reliable, adaptable solution for attendance tracking. It is especially suitable for environments with challenging visibility, providing a robust, real-time alternative to traditional attendance systems. This innovation is ideal for use in academic and industrial settings, where accurate and automated attendance monitoring can improve operational efficiency and data accuracy.
Figure 1. Working methodology details according to the embodiment of the present invention.
[013] Without further elaboration, the foregoing will so fully illustrate my invention, that others may, by applying current of future knowledge, readily adapt the same for use under various conditions of service. It should also be realized by those skilled in the art that such equivalent constructions do not depart from the spirit and scope of the invention.
Advantages over the prior art
[014] Thermal imaging-based employee attendance monitoring system using convolutional neural networks proposed by the present invention has the following advantages over the prior art:
1. Enhanced Accuracy and Reliability:
By using thermal imaging to capture unique heat signatures, the system ensures reliable identification of employees even in low-light conditions or poor visibility due to fog, rain, or other environmental factors. The use of CNNs trained on thermal image data enables high-accuracy recognition, reducing errors in attendance tracking and improving overall reliability.
2. Environmental Adaptability:
Unlike traditional visual recognition systems, which depend on visible facial features, this system remains effective across various environmental conditions, including low light, rain, and fog. The thermal imaging technology can operate indoors and outdoors without additional lighting or environmental controls, making it adaptable to diverse work environments.
3. Privacy Protection:
Since thermal imaging captures only heat signatures without detailed facial features, it offers an inherently privacy-conscious approach, reducing the risk of misuse of personally identifiable information (PII). This makes the system suitable for environments where employee privacy is a priority and can help meet data protection regulations.
4. Cost-Effectiveness:
Integration with Raspberry Pi hardware provides a compact, affordable computing solution for on-site processing, reducing the need for costly servers and infrastructure. The system's reliance on thermal imaging rather than high-resolution video reduces storage and processing requirements, contributing to cost savings in the long term.
5. Real-Time Monitoring and Automated Reporting:
Real-time attendance data is available on a centralized dashboard, enabling immediate monitoring and allowing administrators to quickly identify attendance trends or discrepancies.
The automated reporting feature streamlines attendance management, saving time and minimizing manual errors, and makes integration with HR and payroll systems easy.
6. Scalability:
The system is designed to be easily scalable, allowing for deployment across multiple entry points or facilities. Each Raspberry Pi unit operates independently with connected thermal cameras, so additional devices can be added as needed without overhauling the entire system.
7. Reduced Dependence on Physical Tokens or Cards:
Unlike systems that require RFID cards, badges, or biometric scanning, this system is entirely non-contact and does not rely on physical tokens, reducing the chance of lost cards or forgotten badges. This non-contact approach is more hygienic and reduces potential health concerns related to shared surfaces.
8. Improved Security and Access Control:
By monitoring entry and exit points, the system can also serve as a security measure, helping to restrict unauthorized access. It can be configured to alert administrators if unrecognized individuals attempt entry, providing an added layer of security to the workplace.
[015] In the preceding specification, the invention has been described with reference to specific exemplary embodiments thereof. It will be evident that various modifications and changes may be made thereunto without departing from the broader spirit and scope of the invention as set forth in the claims that follow. The specification and drawings are accordingly to be regarded in an illustrative rather than restrictive sense. Therefore, the aim in the appended claims is to cover all such changes and modifications as fall within the true spirit and scope of the invention. The matter set forth in the foregoing description and accompanying drawings is offered by way of illustration only and not as a limitation. The actual scope of the invention is intended to be defined in the following claims when viewed in their proper perspective based on the prior art.

, Claims:We claim:
1. Thermal imaging-based employee attendance monitoring system using convolutional neural networks applicant comprising of:
- one or more thermal cameras configured to capture thermal images of employees entering and exiting a designated area;
- a computing device with a Convolutional Neural Network (CNN) model, wherein the CNN model is trained on a dataset of thermal images to recognize individuals based on unique thermal patterns;
- wherein the system is configured to process the captured thermal images in real-time to identify employees and log attendance.
2. The system of Claim 1, wherein the CNN model is implemented using a machine learning framework selected from the group consisting of TensorFlow and PyTorch.
3. The system of Claim 1, further comprising a Raspberry Pi device integrated with the thermal camera, wherein the Raspberry Pi device is configured to execute the CNN model for real-time processing of thermal images locally.
4. A method for employee attendance monitoring comprising:
- capturing thermal images of individuals entering and exiting a location using one or more thermal cameras;
- processing the thermal images using a trained Convolutional Neural Network (CNN) model to identify individuals based on their thermal patterns;
- recording attendance data based on the identification of individuals from the thermal images.
5. The method of Claim 4, further comprising displaying real-time attendance data on a centralized dashboard accessible to authorized personnel.
6. The method of Claim 4, wherein the CNN model is trained to recognize thermal patterns of individuals in diverse environmental conditions, including low light, fog, or rain, to ensure reliable identification.
7. A system for employee attendance tracking, comprising:
- a plurality of thermal cameras positioned at entry and exit points;
- a plurality of computing devices, each connected to one or more thermal cameras, wherein each computing device is configured to execute a trained CNN model to process thermal images and identify individuals in real-time;
- a server configured to receive attendance data from each computing device and generate automated attendance reports.
8. The system of Claim 7, wherein each thermal camera captures thermal data that is transmitted to the connected computing device for processing without transmitting personally identifiable information.
9. The system of Claim 7, further comprising a security feature wherein the system generates alerts when thermal images do not match any known thermal pattern in the system database, indicating unauthorized access attempts.
10. A non-contact employee attendance tracking method using thermal imaging, comprising:
- deploying thermal cameras to capture thermal images of individuals entering and exiting;
- processing the thermal images using a CNN model on a computing device, wherein the CNN model is configured to identify individuals based on their unique thermal signatures;
- updating attendance records in real-time based on successful identification, enabling attendance tracking in low visibility and environmentally challenging conditions.

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