Computer Vision in Contactless Biometric
Systems
Farukh Hashmi1, Kiran Ashish2, Satyarth Katiyar3,
and Avinash Keskar4
1Department of Electronics and Communication Engineering, National
Institute of Technology, India
2Computer Vision Engineer, Viume, India
3Department of Electronics and Communication Engineering, Harcourt
Butler Technical University, India
4Department of Electronics and Communication Engineering,
Visvesvaraya National Institute of Technology, India
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.
Keywords: AccessNet, gait
patterns, facial recognition, contactless biometric systems, crucial features,
NvidiaJetsonNano.
Received February 21, 2021; accepted March 7, 2021
https://doi.org/10.34028/iajit/18/3A/12