Computer Vision in Contactless Biometric Systems

Abstract

Contactless biometric systems have increased ever since the corona pandemic outbreak. The two main contactless biometric systems are facial recognition and gait patterns recognition. The authors in the previous work [11] have built hybrid architecture AccessNet. It involves combination of three systems: facial recognition, facial anti-spoofing, and gait recognition. This work involves deploying the hybrid architecture and deploying two individual systems such as facial recognition with facial anti-spoofing and gait recognition individually and comparing the individual results in real-time with the AccessNet hybrid architecture results. This work even involves in identifying the main crucial features from each system that are responsible for predicting a subject. It includes extracting few crucial parameters from gait recognition architecture, facial recognition and facial anti-spoof architectures by visualizing the hidden layers. Each individual method is trained and tested in real-time, which is deployed on both edge device NvidiaJetsonNano, and high-end GPU. A conclusion is also adapted in terms of commercial and research usage for each single method after analysing the real-time test results