A Cascade Face Spoofing Detector Based on Face Anti-Spoofing R-CNN and Improved Retinex LBP

Yaowu Chen,Rongxin Jiang,Xiang Tian,Haonan Chen

doi:10.1109/access.2019.2955383

Abstract

In consideration of secure and convenient, face gains increasing attention in variety of fields during the past decades. Since human face is most accessible from our daily life and preserves the richest information, face based biometric systems are widely used in person authentication applications. However, face recognition systems are always challenged by face spoofing attacks. Although, researchers have proposed many face spoofing detection methods, which have achieved great performances, we aim to develop a method to counter face spoofing, which combines the face detection stage and face spoofing detection stage together. In this paper, we design face anti-spoofing region-based convolutional neural network (FARCNN), based on improved Faster region-based convolutional neural network (R-CNN) framework. Motivated by face detection, we regard the face spoofing detection as a three-way classification to distinguish real face, fake face and background. We extend the typical Faster R-CNN scheme by optimizing several important strategies, including roi-pooling feature fusion and adding Crystal Loss function to the original multi-task loss function. In addition, an improved Retinex based LBP is presented to handle the different illumination conditions in face spoofing detection. Finally, these two detectors are further cascaded and achieve promising performances on the benchmark databases: CASIA-FASD, REPLAY-ATTACK and OULU-NPU. Besides, for the purpose of verifying the generalization capacity of the proposed cascade detector, we perform experiments on cross-databases and the results testify the effectiveness of our proposed method.

Highlights

With applications in person authentication with digital devices, biometric-based systems are widely used with different fine-grained biometric cues
We propose a face anti-spoofing region-based convolutional neural network (R-Convolutional Neural Network (CNN)) (FARCNN) which accepts original face images as the input of the network, which combines the face detection stage and the feature extraction stage together and treat the face spoofing detection as a three-way classification for real face, fake face and background
To take best advantage of the proposed detectors, we present a standby cascade of the FARCNN and the improved Retinex based local binary pattern (LBP) detector, which improves the illumination robustness of the FARCNN and works better for face spoofing detection

Summary

Introduction

With applications in person authentication with digital devices, biometric-based systems are widely used with different fine-grained biometric cues (e.g., fingerprint, iris, motions and face). With the development of the face recognition during the past decades, face gains increasing attention in different kinds of fields, which is most accessible from our daily life and preserves the richest information. In consideration of the information privacy and security, how to counter face spoofing attacks have become an important issue for face-based biometric systems. With the growth of internet and increasing of information leakage, those spoofs are quite easy to obtain, The associate editor coordinating the review of this manuscript and approving it for publication was Zahid Akhtar. It is inescapable to develop reliable face anti-spoofing methods and deploy them in the face authorization systems

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