Exploiting temporal and depth information for multi-frame face anti-spoofing

Face anti-spoofing is significant to the security of face recognition systems. Previous works on depth supervised learning have proved the effectiveness for face anti-spoofing. Nevertheless, they only considered the depth as an auxiliary supervision in the single frame. Different from these methods, we develop a new method to estimate depth information from multiple RGB frames and propose a depth-supervised architecture which can efficiently encodes spatiotemporal information for presentation attack detection. It includes two novel modules: optical flow guided feature block (OFFB) and convolution gated recurrent units (ConvGRU) module, which are designed to extract short-term and long-term motion to discriminate living and spoofing faces. Extensive experiments demonstrate that the proposed approach achieves state-of-the-art results on four benchmark datasets, namely OULU-NPU, SiW, CASIA-MFSD, and Replay-Attack.

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

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

[3]  Weihong Deng,et al.  Learning temporal features using LSTM-CNN architecture for face anti-spoofing , 2015, 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR).

[4]  Sébastien Marcel,et al.  LBP - TOP Based Countermeasure against Face Spoofing Attacks , 2012, ACCV Workshops.

[5]  Dit-Yan Yeung,et al.  Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.

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

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

[8]  Behzad Shahraray,et al.  Robust Depth Estimation From Optical Flow , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[9]  Anderson Rocha,et al.  Face liveness detection under bad illumination conditions , 2011, 2011 18th IEEE International Conference on Image Processing.

[10]  Matti Pietikäinen,et al.  Face spoofing detection from single images using micro-texture analysis , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[11]  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).

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

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

[14]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

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

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

[17]  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).

[18]  Shiv Ram Dubey,et al.  A Performance Evaluation of Convolutional Neural Networks for Face Anti Spoofing , 2018, 2019 International Joint Conference on Neural Networks (IJCNN).

[19]  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.

[20]  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..

[21]  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).

[22]  Wei Zhang,et al.  Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[23]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[24]  Anderson Rocha,et al.  Face Spoofing Detection Through Visual Codebooks of Spectral Temporal Cubes , 2015, IEEE Transactions on Image Processing.

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

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

[27]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

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

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

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

[31]  Pong C. Yuen,et al.  Deep convolutional dynamic texture learning with adaptive channel-discriminability for 3D mask face anti-spoofing , 2017, 2017 IEEE International Joint Conference on Biometrics (IJCB).

[32]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[33]  Eduardo Valle,et al.  Transfer Learning Using Convolutional Neural Networks for Face Anti-spoofing , 2017, ICIAR.

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

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

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