Exploration of neural network approaches in tasks of the face liveness detection in biometrical video monitoring systems

A. B. Mudrich; K. V. Ezhova; I. A. Mazanov

doi:10.1117/12.2687567

27 November 2023 Exploration of neural network approaches in tasks of the face liveness detection in biometrical video monitoring systems

A. B. Mudrich, K. V. Ezhova, I. A. Mazanov

Author Affiliations +

Proceedings Volume 12767, Optoelectronic Imaging and Multimedia Technology X; 127670T (2023) https://doi.org/10.1117/12.2687567
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Conference Poster

Abstract

One of the most actively developing areas of digital image processing algorithms and complex technical vision systems is security systems. The focus of this research is video surveillance systems with biometrical face recognition algorithms. Intelligent analysis of an object (visitor's face) in such a system can be divided into sequential stages - detection (band of the ROI on the image from video), recognition (comparison with a reference photo in a database), and additional functions (for example, tracking, emotion recognition, etc.). At the same time, the rapid development of image and video falsification technologies emphasized the need to protect the system from attempts to fake biometric data used for verification. The techniques of face spoofing include statistically and dynamic two- and three-dimensional attacks, attacks using doubles and avatars, and more. The methods of detecting spoofing are studied in the area of face liveness problems. Neural network architectures and libraries were used as the main methods during research. This research concentrated on the software development for biometrical video monitoring system, which is more secure against face spoofing attacks. For testing algorithms for detecting face liveness, a small real-time video surveillance system prototype consisting of several cameras and providing monitoring of one room was designed. One of the requirements for our system was the use of conventional 2D cameras. For this reason, face liveness detection methods that require the use of 3D cameras or additional sensors were excluded from the field of view at the stage of choosing research methods. This paper presents an analysis of existing approaches to identify face spoofing attacks and compares them with the neural networks-based approach.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

A. B. Mudrich, K. V. Ezhova, and I. A. Mazanov "Exploration of neural network approaches in tasks of the face liveness detection in biometrical video monitoring systems", Proc. SPIE 12767, Optoelectronic Imaging and Multimedia Technology X, 127670T (27 November 2023); https://doi.org/10.1117/12.2687567

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