Low cost enhanced security face recognition with stereo cameras
Biel Tura Vecino, Mart\'i Cobos, Philippe Salembier

TL;DR
This paper presents a low-cost stereo camera-based face recognition system that enhances security by incorporating depth map analysis through deep neural networks, making it resistant to printed photo attacks.
Contribution
It introduces a novel face recognition approach using stereo cameras and deep learning to improve security over existing low-cost solutions.
Findings
Depth map analysis increases security against photo spoofing.
Real-time face authentication achieved with low-cost hardware.
Deep neural networks effectively analyze facial features and depth.
Abstract
This article explores a face recognition alternative which seeks to contribute to resolve current security vulnerabilities in most recognition architectures. Current low cost facial authentication software in the market can be fooled by a printed picture of a face due to the lack of depth information. The presented software creates a depth map of the face with the help of a stereo setup, offering a higher level of security than traditional recognition programs. Analysis of the person's identity and facial depth map are processed through deep convolutional neural networks, providing a secure low cost real-time face authentication method.
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Taxonomy
TopicsFace recognition and analysis · Biometric Identification and Security · Video Surveillance and Tracking Methods
