Optimized deep learning architectures for high precision eye blink detection on consumer grade hardware

Eye blink detection technology has traditionally relied on specialized, high-cost hardware, creating a barrier to its widespread adoption in human-computer interaction and assistive systems. This study challenges that limitation by demonstrating that consumer-grade webcams, when paired with optimized deep learning architectures, can achieve high-precision ocular state classification.(2–5) Three distinct neural network architectures—ResNet, VGG-19, and an application-specific lightweight Convolutional Neural Network—were evaluated and fine-tuned to detect four ocular states (open, closed, left, right) using a standard 5MP webcam.(1–1) To ensure statistical robustness, a rigorous stratified 10-fold cross-validation protocol was applied to a diverse dataset of 3,206 frames collected from 50 participants. The experimental results reveal that computationally efficient models can rival deep architectures; both the ResNet and the custom CNN achieved a high average accuracy of 99.81% (2–14), while the fine-tuned VGG-19 reached 99.66%. Furthermore, robustness tests conducted on low-resolution images confirmed the system’s stability even under significant quality degradation. By demonstrating that accessible hardware can deliver results comparable to high-end sensors under controlled evaluations, this research proposes a highly promising, cost-effective framework. With further large-scale validation, this approach can be scaled for critical applications ranging from driver fatigue monitoring to accessible communication technologies for individuals with disabilities.(2–3).