Multi-Modal Face Anti-Spoofing Attack Detection Challenge at CVPR2019

Anti-spoofing attack detection is critical to guarantee the security of face-based authentication and facial analysis systems. Recently, a multi-modal face anti-spoofing dataset, CASIA-SURF, has been released with the goal of boosting research in this important topic. CASIA-SURF is the largest public data set for facial anti-spoofing attack detection in terms of both, diversity and modalities: it comprises 1,000 subjects and 21,000 video samples. We organized a challenge around this novel resource to boost research in the subject. The Chalearn LAP multi-modal face anti-spoofing attack detection challenge attracted more than 300 teams for the development phase with a total of 13 teams qualifying for the final round. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyze the top ranked solutions and draw conclusions derived from the competition. In addition we outline future work directions.

[1]  Lin Sun,et al.  Eyeblink-based Anti-Spoofing in Face Recognition from a Generic Webcamera , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[2]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[3]  丁晓青,et al.  Face Live Detection Method Based on Physiological Motion Analysis , 2009 .

[4]  Matti Pietikäinen,et al.  Competition on counter measures to 2-D facial spoofing attacks , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[5]  Lin Sun,et al.  Monocular camera-based face liveness detection by combining eyeblink and scene context , 2011, Telecommun. Syst..

[6]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[7]  Jukka Komulainen,et al.  The 2nd competition on counter measures to 2D face spoofing attacks , 2013, 2013 International Conference on Biometrics (ICB).

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

[9]  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).

[10]  Shengcai Liao,et al.  Learning Face Representation from Scratch , 2014, ArXiv.

[11]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[12]  Lai-Man Po,et al.  Integration of image quality and motion cues for face anti-spoofing: A neural network approach , 2016, J. Vis. Commun. Image Represent..

[13]  Sébastien Marcel,et al.  Face Recognition Systems Under Spoofing Attacks , 2016, Face Recognition Across the Imaging Spectrum.

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

[15]  Yu Qiao,et al.  A Discriminative Feature Learning Approach for Deep Face Recognition , 2016, ECCV.

[16]  Gang Hua,et al.  Ordinal Regression with Multiple Output CNN for Age Estimation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Yuxiao Hu,et al.  MS-Celeb-1M: Challenge of Recognizing One Million Celebrities in the Real World , 2016, IMAWM.

[18]  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).

[19]  Yi Zhu,et al.  DenseNet for dense flow , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[20]  Fei Peng,et al.  Face presentation attack detection using guided scale texture , 2017, Multimedia Tools and Applications.

[21]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[22]  Zhuowen Tu,et al.  Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Shuai Yi,et al.  FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction , 2019, NeurIPS.

[24]  Fei Peng,et al.  CCoLBP: Chromatic Co-Occurrence of Local Binary Pattern for Face Presentation Attack Detection , 2018, 2018 27th International Conference on Computer Communication and Networks (ICCCN).

[25]  Omkar M. Parkhi,et al.  VGGFace2: A Dataset for Recognising Faces across Pose and Age , 2017, 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018).

[26]  Fang Zhao,et al.  Towards Pose Invariant Face Recognition in the Wild , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[27]  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.

[28]  Xiangyu Zhang,et al.  ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design , 2018, ECCV.

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

[30]  Sébastien Marcel,et al.  Spoofing Deep Face Recognition with Custom Silicone Masks , 2018, 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS).

[31]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[32]  In-So Kweon,et al.  CBAM: Convolutional Block Attention Module , 2018, ECCV.

[33]  Tieniu Tan,et al.  A Light CNN for Deep Face Representation With Noisy Labels , 2015, IEEE Transactions on Information Forensics and Security.

[34]  Roberto Javier López-Sastre,et al.  Generalized Presentation Attack Detection: a face anti-spoofing evaluation proposal , 2019, 2019 International Conference on Biometrics (ICB).

[35]  Shifeng Zhang,et al.  A Dataset and Benchmark for Large-Scale Multi-Modal Face Anti-Spoofing , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.