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FACIAL EYE RECOGNITION FOR PHYSICALLY CHALLENGED PERSON USING DEEP LEARNING

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FACIAL EYE RECOGNITION FOR PHYSICALLY CHALLENGED PERSON USING DEEP LEARNING

ORDINARY APPLICATION

Published

date

Filed on 14 November 2024

Abstract

The innovation in software for Facial Eye Recognition for Physically Challenged Persons using Deep Learning resides within the intersection of computer vision, artificial intelligence (AI), and assistive technology domains. Computer vision is the core technical domain underpinning this innovation. It involves the development of algorithms and techniques to enable computers to interpret and understand visual information from the real world. Deep learning, a subset of machine learning, plays a crucial role in this innovation. Deep learning techniques, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are employed to train models on large datasets of facial images and eye movements. Facial recognition technology involves the identification or verification of individuals based on their facial features. Eye tracking technology enables the measurement and analysis of eye movements and gaze patterns.

Patent Information

Application ID202441087980
Invention FieldCOMPUTER SCIENCE
Date of Application14/11/2024
Publication Number47/2024

Inventors

NameAddressCountryNationality
K. SundaresanAssistant Professor Department of Computer Science and Engineering, Karpagam Institute of Technology, Bodipalayam Post, Seerapalayam Village, CoimbatoreIndiaIndia
G. G. JayacharanFinal Year Student, Department of Computer Science and Engineering, Karpagam Institute of Technology, Bodipalayam Post, Seerapalayam Village, CoimbatoreIndiaIndia
S. PallaviFinal Year Student, Department of Computer Science and Engineering, Karpagam Institute of Technology, Bodipalayam Post, Seerapalayam Village, CoimbatoreIndiaIndia
U. SonaFinal Year Student, Department of Computer Science and Engineering, Karpagam Institute of Technology, Bodipalayam Post, Seerapalayam Village, CoimbatoreIndiaIndia

Applicants

NameAddressCountryNationality
Karpagam Institute of TechnologyS.F.NO.247,248, Bodipalayam Post, Seerapalayam Village, CoimbatoreIndiaIndia
Karpagam Academy of Higher EducationPollachi Main Road, Eachanari Post, CoimbatoreIndiaIndia
K. SundaresanAssistant Professor Department of Computer Science and Engineering, Karpagam Institute of Technology, Bodipalayam Post, Seerapalayam Village, CoimbatoreIndiaIndia
G. G. JayacharanFinal Year Student, Department of Computer Science and Engineering, Karpagam Institute of Technology, Bodipalayam Post, Seerapalayam Village, CoimbatoreIndiaIndia
S. PallaviFinal Year Student, Department of Computer Science and Engineering, Karpagam Institute of Technology, Bodipalayam Post, Seerapalayam Village, CoimbatoreIndiaIndia
U. SonaFinal Year Student, Department of Computer Science and Engineering, Karpagam Institute of Technology, Bodipalayam Post, Seerapalayam Village, CoimbatoreIndiaIndia

Specification

Description:Technical field

The intersection of computer vision, artificial intelligence (AI), and deep learning. It utilizes convolutional neural networks (CNNs) for processing and analyzing facial images, enabling accurate eye movement recognition. Recurrent neural networks (RNNs) are employed to capture temporal dynamics in gaze patterns, enhancing the system's responsiveness. The project focuses on developing algorithms that facilitate real-time eye tracking for assistive technology applications. This integration of advanced machine learning techniques supports the creation of innovative solutions for improving accessibility for physically challenged individuals.

Background

Introduction to Accessibility Technology: Accessibility technology aims to enhance the quality of life for physically challenged individuals by enabling them to interact with their environment more effectively. Among various assistive technologies, eye-tracking systems hold significant potential.
Importance of Eye Movement: Eye movement serves as a powerful means of communication for individuals with limited mobility. Recognizing and interpreting these movements can facilitate control over devices, aiding in daily activities and improving independence.
Challenges Faced by Physically Challenged Individuals: Many physically challenged persons struggle with traditional input methods such as keyboards and mice. This highlights the need for innovative solutions that cater to their unique requirements, promoting inclusivity.
Introduction to Facial Recognition Technology: Facial recognition technology has advanced rapidly, leveraging machine learning and computer vision to identify and analyze facial features. These technologies can be adapted to recognize eye movements for various applications.
Deep Learning in Computer Vision: Deep learning, particularly convolutional neural networks (CNNs), has revolutionized the field of computer vision. By learning hierarchical features from raw data, deep learning models can achieve high accuracy in recognizing patterns, including facial expressions and eye movements.
Eye Tracking Techniques: Traditional eye tracking methods often rely on specialized hardware, such as infrared cameras. However, advancements in deep learning enable the use of standard cameras for accurate eye movement recognition, making the technology more accessible.
Benefits of Deep Learning for Eye Recognition: Deep learning algorithms can process large amounts of image data and learn to identify subtle eye movements. This capability enhances the precision and robustness of eye-tracking systems for individuals with disabilities.
Facial Landmark Detection: To effectively recognize eye movements, facial landmark detection algorithms identify key facial points, such as the eyes, nose, and mouth. This information is critical for understanding the orientation and movement of the eyes in relation to the user's intentions.
Model Training and Data Requirements: Developing an effective eye recognition system requires extensive datasets that include diverse examples of eye movements across different individuals. This data is crucial for training deep learning models to generalize well across various users.
Real-Time Processing Capabilities: For eye recognition systems to be practical, they must operate in real time. Deep learning models can be optimized for efficiency, enabling quick responses to user commands and enhancing the overall user experience.
User Interface Design: The interface for eye-controlled systems must be intuitive and user-friendly, accommodating varying levels of ability. Effective design can significantly improve the accessibility and usability of the technology for physically challenged users.
Potential Applications: Eye recognition technology can be applied in numerous contexts, including communication aids, smart home systems, and assistive devices for education. These applications empower users by granting them greater control over their environment.
Ethical Considerations: Implementing eye recognition technology raises ethical considerations, particularly regarding privacy and data security. It is essential to ensure that user data is handled responsibly and that systems are designed with user consent in mind.
Future Developments: As deep learning techniques continue to evolve, the accuracy and capabilities of eye recognition systems are expected to improve. Research into new algorithms and model architectures will likely yield even more effective solutions for assisting physically challenged individuals.
Conclusion: The integration of deep learning for facial eye recognition presents a promising avenue for enhancing accessibility for physically challenged persons. By leveraging advanced technologies, these systems can significantly improve independence and quality of life, fostering a more inclusive society.

Summary of the Invention

Facial Eye Recognition for Physically Challenged Persons Using Deep Learning is a cutting-edge software invention that aims to empower individuals with physical disabilities by providing them with advanced assistive technology. Leveraging the capabilities of deep learning algorithms, particularly convolution neural networks (CNNs), this software offers a groundbreaking solution for recognizing facial expressions and eye movements to enable hands-free interaction with digital devices and interfaces.
The aim of obtaining a software patent for Facial Eye Recognition for Physically Challenged Persons using Deep Learning is to protect, commercialize, and promote the innovative technology, while simultaneously driving further innovation, enhancing accessibility, and fostering collaboration within the assistive technology ecosystem.
The software innovation, Facial Eye Recognition for Physically Challenged Persons Using Deep Learning, empowers individuals with physical disabilities by enabling hands-free interaction with digital devices and interfaces. Through advanced deep learning algorithms, the software recognizes facial expressions and tracks eye movements in real-time, allowing users to navigate menus, scroll through content, and perform actions using gestures and commands. Customizable and user-friendly, this innovation enhances independence, accessibility, and efficiency for individuals with physical challenges.
Facial Eye Recognition for Physically Challenged Persons Using Deep Learning represents a groundbreaking software invention that leverages the capabilities of deep learning algorithms to empower individuals with physical disabilities. By enabling hands-free interaction with digital interfaces through facial and eye recognition, this innovative software enhances independence, accessibility, and quality of life for users, marking a significant advancement in the field of assistive technology.
The system architecture for the project is designed to provide an interface for individuals with physical challenges. It begins with an Input Module, capturing real-time video from a mounted camera. The Face Detection and Tracking module identifies and tracks faces within the video frames, while the subsequent Eye Region Extraction module isolates the eyes for focused analysis. The Eye Movement Recognition Model, a deep learning component, processes the pre-processed eye images to predict gaze direction. Optionally, a Facial Expression Recognition module can be included to offer additional context. The User Interface module integrates these components, displaying the video feed and overlaying relevant information, allowing users to interact via their eye movements.
, Claims:1. Integration of Multiple Technologies: The project uniquely combines computer vision, deep learning, and assistive technology to create a comprehensive system specifically designed for physically challenged individuals, enhancing both accessibility and functionality.
2. Use of Standard Cameras: By employing standard cameras for eye tracking instead of specialized hardware, the project makes advanced eye recognition technology more accessible and affordable for users, eliminating barriers to entry.
3. Real-Time Processing: The system focuses on real-time processing of eye movements, ensuring immediate feedback and interaction, which is crucial for user experience and effectiveness in assistive applications.
4. User-Centric Design: The project prioritizes a user-friendly interface that caters to a diverse range of physical abilities, ensuring that individuals can easily navigate and control devices through eye movements.
5. Enhanced Model Training: Utilizing convolutional neural networks (CNNs) and recurrent neural networks (RNNs) enables the model to learn from extensive datasets, leading to improved accuracy in recognizing subtle eye movements and enhancing the system's overall performance.

Documents

NameDate
202441087980-COMPLETE SPECIFICATION [14-11-2024(online)].pdf14/11/2024
202441087980-DECLARATION OF INVENTORSHIP (FORM 5) [14-11-2024(online)].pdf14/11/2024
202441087980-DRAWINGS [14-11-2024(online)].pdf14/11/2024
202441087980-EDUCATIONAL INSTITUTION(S) [14-11-2024(online)].pdf14/11/2024
202441087980-EVIDENCE FOR REGISTRATION UNDER SSI [14-11-2024(online)].pdf14/11/2024
202441087980-EVIDENCE FOR REGISTRATION UNDER SSI(FORM-28) [14-11-2024(online)].pdf14/11/2024
202441087980-FIGURE OF ABSTRACT [14-11-2024(online)].pdf14/11/2024
202441087980-FORM 1 [14-11-2024(online)].pdf14/11/2024
202441087980-FORM FOR SMALL ENTITY(FORM-28) [14-11-2024(online)].pdf14/11/2024
202441087980-FORM-9 [14-11-2024(online)].pdf14/11/2024
202441087980-REQUEST FOR EARLY PUBLICATION(FORM-9) [14-11-2024(online)].pdf14/11/2024

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