Plenoptic Face Presentation Attack Detection.

Shuaishuai Zhu,Jie Lin,Xiaohua Feng,Peng Jin,Liang Gao,Xiaobo Lv

doi:10.1109/access.2020.2980755

Abstract

The vulnerability of current face recognition systems to presentation attacks significantly limits their application in biometrics. Herein, we present a passive presentation attack detection method based on a complete plenoptic imaging system which can derive the complete plenoptic function of light rays using a single detector. Moreover, we constructed a multi-dimensional face database with 50 subjects and seven different types of presentation attacks. We experimentally demonstrated that our approach outperforms the state-of-the-art methods on all types of presentation attacks.

Highlights

Fast and non-intrusive, face recognition has been extensively used in a broad spectrum of applications, such as border control, national ID control, and personal device access [1], [2]
We present a passive presentation attack detection (PAD) method based on complete plenoptic imaging [24]
According to the ISO/IEC 30107-3:2017 [40], we evaluated the performance of the PAD methods using two metrics: i) attack presentation classification error rate (APCER) which measures the proportion of attack presentations using the same presentation attack instruments (PAI) species incorrectly classified as bona fide ones, and

Summary

Introduction

Fast and non-intrusive, face recognition has been extensively used in a broad spectrum of applications, such as border control, national ID control, and personal device access [1], [2]. The PAD methods based on multi-spectral imaging [18]–[20] and light field imaging [21]–[23] measure the reflectance of living skin and the 3D profile of a bona fide face, respectively. Our plenoptic PAD combines the advantages of the multi-spectral and light-field PAD methods to enhance the robustness of a face recognition system against various types of presentation attacks.

Results

Conclusion