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This paper presents a real-time video-based system for detecting squint and cataract using advanced computer vision techniques, specifically leveraging a media-pipe face-mesh model for ocular feature extraction. The system achieves high accuracy rates, with 98.39% for squint detection and 96.90% for cataract classification, making it suitable for low-cost, large-scale deployment using standard cameras. By enabling automatic ocular analysis, the framework aims to enhance web accessibility for individuals with visual impairments, paving the way for adaptive user interfaces.
Achieving over 98% accuracy in squint and cataract detection, this system could revolutionize accessibility in web interfaces for the visually impaired.
Squint and cataract are major ocular disorders that majorly affect visual perception and interaction capability. This paper proposes a real-time video-based automated detection system for squint and cataract detection based on computer vision and image processing methods. The proposed system uses a media-pipe face-mesh (a 478-point facial landmark detection model) to extract geometric ocular features for multi-class squint classification. Simultaneously, The presence and severity cataract is estimated through grayscale intensity and histogram-based lens opacity analysis. The system records short video sequences with standard laptop or mobile cameras, which can be deployed at low costs and on a large scale. The experimental performance has shown great accuracy in the detection of squint (98.39%) and classification of cataract (96.90%). Besides automatic ocular analysis, the proposed framework is also made accessible for visual impairment inference which will be integrated with future adaptive user interface and Web accessibility systems for people with visual impairment.
