3DPC-Net: 3D Point Cloud Network for Face Anti-spoofing

Face anti-spoofing plays a vital role in face recognition systems. Most deep learning-based methods directly use 2D images assisted with temporal information (i.e., motion, rPPG) or pseudo-3D information (i.e., Depth). The main drawback of the mentioned methods is that another extra network is needed to generate the depth/rPPG information to assist the backbone network for face anti-spoofing. Different from these methods, we propose a novel method named 3D Point Cloud Network (3DPC-Net). It is an encoder-decoder network that can predict the 3DPC maps to discriminate live faces from spoofing ones. The main traits of the proposed method are that: 1) It is the first time that 3DPC is used for face anti-spoofing; 2) 3DPC-Net is simple and effective and it only relies on 3DPC supervision. Extensive experiments on four databases (i.e., Oulu-NPU, SiW, CASIA-FASD, Replay Attack) have demonstrated that the 3DPC-Net is comparative to the state-of-the-art methods.

[1]  Xiaoming Liu,et al.  Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[2]  Pong C. Yuen,et al.  Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Anderson Rocha,et al.  Video-Based Face Spoofing Detection through Visual Rhythm Analysis , 2012, 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images.

[4]  Jukka Komulainen,et al.  Face anti-spoofing based on color texture analysis , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[5]  Abdenour Hadid,et al.  An original face anti-spoofing approach using partial convolutional neural network , 2016, 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA).

[6]  Chenxu Zhao,et al.  Searching Central Difference Convolutional Networks for Face Anti-Spoofing , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Jukka Komulainen,et al.  OULU-NPU: A Mobile Face Presentation Attack Database with Real-World Variations , 2017, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).

[8]  Yi Li,et al.  Face Liveness Detection from a Single Image with Sparse Low Rank Bilinear Discriminative Model , 2010, ECCV.

[9]  Yue Wang,et al.  Dynamic Graph CNN for Learning on Point Clouds , 2018, ACM Trans. Graph..

[10]  Xiangyu Zhu,et al.  Deep Spatial Gradient and Temporal Depth Learning for Face Anti-Spoofing , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Sébastien Marcel,et al.  Can face anti-spoofing countermeasures work in a real world scenario? , 2013, 2013 International Conference on Biometrics (ICB).

[12]  Sébastien Marcel,et al.  Spoofing in 2D face recognition with 3D masks and anti-spoofing with Kinect , 2013, 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[13]  Shifeng Zhang,et al.  CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing , 2019, IEEE Transactions on Biometrics, Behavior, and Identity Science.

[14]  Sébastien Marcel,et al.  On the effectiveness of local binary patterns in face anti-spoofing , 2012, 2012 BIOSIG - Proceedings of the International Conference of Biometrics Special Interest Group (BIOSIG).

[15]  Sébastien Marcel,et al.  Domain Adaptation in Multi-Channel Autoencoder based Features for Robust Face Anti-Spoofing , 2019, 2019 International Conference on Biometrics (ICB).

[16]  Anil K. Jain,et al.  Secure Face Unlock: Spoof Detection on Smartphones , 2016, IEEE Transactions on Information Forensics and Security.

[17]  C. Qi.  Deep Learning on Point Sets for 3 D Classification and Segmentation , 2016 .

[18]  Junjie Yan,et al.  A face antispoofing database with diverse attacks , 2012, 2012 5th IAPR International Conference on Biometrics (ICB).

[19]  Fei Peng,et al.  A competition on generalized software-based face presentation attack detection in mobile scenarios , 2017, 2017 IEEE International Joint Conference on Biometrics (IJCB).

[20]  Anil K. Jain,et al.  Cross-Database Face Antispoofing with Robust Feature Representation , 2016, CCBR.

[21]  Jukka Komulainen,et al.  Face Antispoofing Using Speeded-Up Robust Features and Fisher Vector Encoding , 2017, IEEE Signal Processing Letters.

[22]  Mathieu Aubry,et al.  AtlasNet: A Papier-M\^ach\'e Approach to Learning 3D Surface Generation , 2018, CVPR 2018.

[23]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Xiaoming Liu,et al.  Face De-Spoofing: Anti-Spoofing via Noise Modeling , 2018, ECCV.

[25]  Jukka Komulainen,et al.  Face Spoofing Detection Using Colour Texture Analysis , 2016, IEEE Transactions on Information Forensics and Security.

[26]  Matti Pietikäinen,et al.  Context based face anti-spoofing , 2013, 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[27]  Xiaogang Wang,et al.  Deep Learning Face Representation from Predicting 10,000 Classes , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Xi Zhou,et al.  Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network , 2018, ECCV.

[29]  Shengcai Liao,et al.  Face liveness detection with component dependent descriptor , 2013, 2013 International Conference on Biometrics (ICB).

[30]  Junying Gan,et al.  3D Convolutional Neural Network Based on Face Anti-spoofing , 2017, 2017 2nd International Conference on Multimedia and Image Processing (ICMIP).

[31]  Samarth Bharadwaj,et al.  Computationally Efficient Face Spoofing Detection with Motion Magnification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[32]  Anjith George,et al.  Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection , 2019, 2019 International Conference on Biometrics (ICB).

[33]  Wenhan Luo,et al.  Face Anti-Spoofing: Model Matters, so Does Data , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  Xiaoming Liu,et al.  Face anti-spoofing using patch and depth-based CNNs , 2017, 2017 IEEE International Joint Conference on Biometrics (IJCB).

[35]  Stan Z. Li,et al.  Learn Convolutional Neural Network for Face Anti-Spoofing , 2014, ArXiv.

[36]  Xiangyu Zhu,et al.  Improving Face Anti-Spoofing by 3D Virtual Synthesis , 2019, 2019 International Conference on Biometrics (ICB).

[37]  Xiaoming Liu,et al.  Face Alignment in Full Pose Range: A 3D Total Solution , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Leonidas J. Guibas,et al.  PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[39]  Leonidas J. Guibas,et al.  PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space , 2017, NIPS.