Fast and Robust Passive Live Face Detection for Access Control Utilizing Stereo Vision and Deep Learning

Abstract

This study presents a novel approach for fast and robust passive live face detection designed for enhanced access control systems. The proposed solution integrates stereo vision technology with advanced deep learning models to deliver a high-performance authentication mechanism. Utilizing the OAK-D Lite stereo vision camera, the system captures both 2D and 3D facial data, enabling the extraction of depth information to distinguish between live human faces and spoofing attempts such as photos, videos, or masks. A custom deep learning model is developed, specifically optimized to recognize subtle depth variations in real time. The study emphasizes the model’s capability to operate without active user engagement, such as blinking or head movements, providing a seamless and secure authentication process. To train the model, a comprehensive dataset of stereo facial images was collected, followed by meticulous data preprocessing and augmentation. The training process included the use of state-of-the-art deep learning techniques, leveraging stereo-view depth information to improve the model's robustness against spoofing attacks. The performance of the model was evaluated based on several metrics, including accuracy, precision, recall, and F1 score, showcasing a significant improvement in spoof detection accuracy when compared to traditional 2D facial recognition systems. The results indicate that the integration of stereo vision and deep learning dramatically reduces the false acceptance rate in face authentication systems while maintaining real time processing capabilities crucial for access control environments. By addressing key limitations in current facial recognition technology, this research contributes a highly secure and efficient face authentication method, paving the way for its application in industries requiring rigorous access control such as banking, healthcare, and corporate security.