Multi-Angle Facial Recognition: Enhancing Biometric Security with a Broadly Positioned Stereo-Camera System
Muhamad Amirul Haq(a*), Le Nam Quoc Huy(b), Muhammad Ridlwan(c), Ishmatun Naila(d)

(a) Department of Computer Science, Faculty of Engineering, Universitas Muhammadiyah Surabaya, Surabaya, Indonesia
* amirulhaq[at]ft.um-surabaya.ac.id
(b) Department of Mechanical Engineering, College of Engineering, National Taiwan University of Science and Technology, Taipei City, Taiwan
(c) Department of Indonesian Language and Literature, Faculty of Teacher Training and Education, Universitas Muhammadiyah Surabaya, Surabaya, Indonesia,
(d) Primary School Education Program, Faculty of Teacher Training and Education, Universitas Muhammadiyah Surabaya, Surabaya, Indonesia

Abstract

This study addresses the vulnerabilities of traditional monocular camera-based face recognition systems, emphasizing the need for improved security and reliability in biometric authentication under varying conditions and human poses. To counteract the risk of spoofing attacks using masks or static images, we introduce a multi-angle stereo camera system. This system is strategically designed to capture facial imagery from multiple perspectives, thereby enhancing depth perception and spatial accuracy, crucial for high-security authentication. Employing a novel image processing approach, the study integrates a hybrid model of Convolutional Neural Networks and Transformers. This combination exploits CNN^s robust feature extraction capabilities with the Transformers^ generalization prowess, enabling precise detection and analysis of 3D facial landmarks. Such an approach significantly bolsters the system^s ability to differentiate between genuine faces and deceptive representations like masks or static images. Empirical results demonstrate that the stereo camera configuration substantially improves recognition accuracy, reducing both false positives and negatives, especially in controlled spoofing scenarios. The advanced 3D facial landmark detection further reinforces the system^s security. With its enhanced robustness and security, the developed system shows great potential for applications in areas requiring stringent identity verification, such as banking, public facilities, and smart home technologies.

Keywords: Stereo Camera- Face Recognition- Biometric Authentication- Deep Learning- Facial Landmark.

Topic: Engineering