A 3D Mask Presentation Attack Detection Method Based on Polarization Medium Wave Infrared Imaging

Dan Zeng,Xiaoyan Li,Pengcheng Sun,Liyuan Li,Lin Yang,Fansheng Chen,Zhouxia Chen

doi:10.3390/sym12030376

Abstract

Facial recognition systems are often spoofed by presentation attack instruments (PAI), especially by the use of three-dimensional (3D) face masks. However, nonuniform illumination conditions and significant differences in facial appearance will lead to the performance degradation of existing presentation attack detection (PAD) methods. Based on conventional thermal infrared imaging, a PAD method based on the medium wave infrared (MWIR) polarization characteristics of the surface material is proposed in this paper for countering a flexible 3D silicone mask presentation attack. A polarization MWIR imaging system for face spoofing detection is designed and built, taking advantage of the fact that polarization-based MWIR imaging is not restricted by external light sources (including visible light and near-infrared light sources) in spite of facial appearance. A sample database of real face images and 3D face mask images is constructed, and the gradient amplitude feature extraction method, based on MWIR polarization facial images, is designed to better distinguish the skin of a real face from the material used to make a 3D mask. Experimental results show that, compared with conventional thermal infrared imaging, polarization-based MWIR imaging is more suitable for the PAD method of 3D silicone masks and shows a certain robustness in the change of facial temperature.

Highlights

Biometric techniques have become a part of daily life, and the most widely used technique is facial recognition
Biometric features or objects used in a face presentation attack are called presentation attack instruments (PAI) [ISO/IEC JTC1 SC37 Biometrics 2016] [3,4,5]
To evaluate the presentation attack detection (PAD) performance, this paper uses three old evaluation metrics and two new metrics defined by the ISO/IEC 30107-3 standard, namely the attack presentation classification error rate (APCER) and the bona fide presentation classification (BPCER) error rate

Summary

Introduction

Biometric techniques have become a part of daily life, and the most widely used technique is facial recognition. The vulnerability of the data capture subsystem, and even the whole system in general, greatly reduces the security of facial recognition applications [1]. Face presentation attack [2,3] creates this problem. Biometric features or objects used in a face presentation attack are called presentation attack instruments (PAI) [ISO/IEC JTC1 SC37 Biometrics 2016] [3,4,5]. Facial presentation attacks mainly originate from three types of PAI: photos of a whole face, replaying videos of a face, and three-dimensional (3D) masks [3]. Many researchers are working on research of the presentation attack detection (PAD)

Methods

Results

Conclusion