Published on Wed Mar 27 2019

3D Face Mask Presentation Attack Detection Based on Intrinsic Image Analysis

Lei Li, Zhaoqiang Xia, Xiaoyue Jiang, Yupeng Ma, Fabio Roli, Xiaoyi Feng

Face presentation attacks have become a major threat to face recognition systems. Unlike the real face, the 3D face mask is usually made of resin materials and has a smooth surface, resulting in reflectance differences. In the proposed method, the face image is first processed with intrinsic

0
0
0
Abstract

Face presentation attacks have become a major threat to face recognition systems and many countermeasures have been proposed in the past decade. However, most of them are devoted to 2D face presentation attacks, rather than 3D face masks. Unlike the real face, the 3D face mask is usually made of resin materials and has a smooth surface, resulting in reflectance differences. So, we propose a novel detection method for 3D face mask presentation attack by modeling reflectance differences based on intrinsic image analysis. In the proposed method, the face image is first processed with intrinsic image decomposition to compute its reflectance image. Then, the intensity distribution histograms are extracted from three orthogonal planes to represent the intensity differences of reflectance images between the real face and 3D face mask. After that, the 1D convolutional network is further used to capture the information for describing different materials or surfaces react differently to changes in illumination. Extensive experiments on the 3DMAD database demonstrate the effectiveness of our proposed method in distinguishing a face mask from the real one and show that the detection performance outperforms other state-of-the-art methods.

Mon Mar 22 2021
Computer Vision
Improved Detection of Face Presentation Attacks Using Image Decomposition
0
0
0
Tue Jun 09 2020
Computer Vision
Detection of Makeup Presentation Attacks based on Deep Face Representations
The application of makeup can be abused to launch so-called makeup presentation attacks. In such attacks, the attacker might apply heavy makeup in order to achieve the facial appearance of a target for the purpose of impersonation. The proposed detection system employs a machine learning-based classifier.
0
0
0
Tue May 12 2020
Computer Vision
3D Face Anti-spoofing with Factorized Bilinear Coding
3D face spoofing attacks are more challenging because face recognition systems are more easily confused by the 3Dcharacteristics of materials similar to real faces. We propose a novel anti-spoofing method based on factorized bilinear coding of multiple color channels.
0
0
0
Mon Jul 29 2019
Computer Vision
Specular- and Diffuse-reflection-based Face Spoofing Detection for Mobile Devices
Face spoofing attacks are a critical issue for the safe deployment of face recognition systems. We propose an efficient face presentation attack detection algorithm. It requires minimal hardware and only a small database, making it suitable for resource-constrained devices.
0
0
0
Thu Feb 07 2019
Computer Vision
FaceSpoof Buster: a Presentation Attack Detector Based on Intrinsic Image Properties and Deep Learning
Presentation attack detection is a crucial step for preventing unauthorized accesses into restricted areas or devices. The proposed method is able to overpass state-of-the-art results in an inter-dataset protocol, which is the most challenging in the literature.
0
0
0
Thu Sep 19 2019
Computer Vision
Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Network
Face recognition is a mainstream biometric authentication method. However, vulnerability to presentation attacks (a.k.a spoofing) limits its usability in unsupervised applications. We propose amulti-channel Convolutional Neural Network based approach for presentation attack detection.
0
0
0