Face anti-spoofing with joint spoofing medium detection and eye blinking analysis

Автор: Nikitin Mikhail Yurievich, Konushin Vadim Sergeyevich, Konushin Anton Sergeyevich

Журнал: Компьютерная оптика @computer-optics

Рубрика: Обработка изображений, распознавание образов

Статья в выпуске: 4 т.43, 2019 года.

Бесплатный доступ

Modern biometric systems based on face recognition demonstrate high recognition quality, but they are vulnerable to face presentation attacks, such as photo or replay attack. Existing face anti-spoofing methods are mostly based on texture analysis and due to lack of training data either use hand-crafted features or ﬁne-tuned pretrained deep models. In this paper we present a novel CNN-based approach for face anti-spoofing, based on joint analysis of the presence of a spoofing medium and eye blinking. For training our classifiers we propose the procedure of synthetic data generation which allows us to train powerful deep models from scratch. Experimental analysis on the challenging datasets (CASIA-FASD, NUUA Imposter) shows that our method can obtain state-of-the-art results.

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Face anti-spoofing, synthetic data, video analysis, neural networks, deep learning

Короткий адрес: https://sciup.org/140246494

IDR: 140246494 | DOI: 10.18287/2412-6179-2019-43-4-618-626

Список литературы Face anti-spoofing with joint spoofing medium detection and eye blinking analysis

Zhang Z, Yan J, Liu S, Lei Z, Yi D, Li SZ. A face antispooﬁng database with diverse attacks. 5th IEEE International Conference on Biometrics (ICB) 2012: 26-31.
Tan X, Li Y, Liu J, Jiang L. Face liveness detection from a single image with sparse low rank bilinear discriminative model. European Conference on Computer Vision (ECCV) 2010: 504-517.
Komulainen J, Hadid A, Pietikainen M. Context based face anti-spooﬁng. 6th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) 2013: 1-8.
Yang J, Lei Z, Liao S, Li SZ. Face liveness detection with component dependent descriptor. 6th IEEE International Conference on Biometrics (ICB) 2013: 1-6.
De Freitas Pereira T, Komulainen J, Anjos A, De Martino JM, Hadid A, Pietikinen M, Marcel S. Face liveness detection using dynamic texture. EURASIP Journal on Image and Video Processing 2014: 2.
Peixoto B, Michelassi C, Rocha A. Face liveness detection under bad illumination conditions. 18th IEEE International Conference on Image Processing (ICIP) 2011: 3557-3560.
Patel K, Han H, Jain A. Secure face unlock: Spoof detection on smartphones. IEEE transactions on information forensics and security 2016; 11(10): 2268-2283.
Komulainen J, Hadid A, Pietikinen M. Face spooﬁng detection using dynamic texture. Asian Conference on Computer Vision (ACCV) 2012: 146-157.
Boulkenafet Z, Komulainen J, Hadid A. Face anti-spooﬁng based on color texture analysis. 22nd IEEE International Conference on Image Processing (ICIP) 2015: 2636-2640.
De Freitas Pereira T, Anjos A, De Martino JM, Marcel S. LBP-TOP based countermeasure against face spooﬁng attacks. Asian Conference on Computer Vision (ACCV) 2012: 121-132.
De Freitas Pereira T, Anjos A, De Martino JM, Marcel S. Can face anti-spooﬁng countermeasures work in a real world scenario? 6th IEEE International Conference on Biometrics (ICB) 2013: 1-8.
Boulkenafet Z, Komulainen J, Hadid A. Face anti-spooﬁng using speeded-up robust features and ﬁsher vector encoding. IEEE Signal Processing Letters 2017; 24(2): 141-145.
Atoum Y, Liu Y, Jourabloo A, Liu X. Face anti-spooﬁng using patch and depth-based CNNs. IEEE International Joint Conference on Biometrics (IJCB) 2017: 319-328.
Pan G, Sun L, Wu Z, Lao S. Eyeblink-based anti-spooﬁng in face recognition from a generic webcamera. 11th IEEE International Conference on Computer Vision (ICCV) 2007: 1-8.
Sun L, Pan G, Wu Z, Lao S. Blinking-based live face detection using conditional random ﬁelds. International Conference on Biometrics (ICB) 2007: 252-260.
Patel K, Han H, Jain AK. Cross-database face anti-spooﬁng with robust feature representation. Chinese Conference on Biometric Recognition 2016: 611-619.
Kollreider K, Fronthaler H, Faraj MI, Bigun J. Real-time face detection and motion analysis with application in liveness assessment. IEEE Transactions on Information Forensics and Security 2007; 2(3): 548-558.
Shao R, Lan X, Yuen PC. Deep convolutional dynamic texture learning with adaptive channel-discriminability for 3D mask face anti-spooﬁng. IEEE International Joint Conference on Biometrics (IJCB) 2017: 748-755.
Kollreider K, Fronthaler H, Bigun J. Non-intrusive liveness detection by face images. Image and Vision Computing 2009; 27(3): 233-244.
Bao W, Li H, Li N, Jiang W. A liveness detection method for face recognition based on optical ﬂow ﬁeld. IEEE Image Analysis and Signal Processing (IASP) 2009: 233-236.
Bharadwaj S, Dhamecha TI, Vatsa M, Singh R. Face antispooﬁng via motion magniﬁcation and multifeature videolet aggregation. 2014. Source: áhttps://repository.iiitd.edu.in/jspui/handle/123456789/138ñ.
Feng L, Po LM, Li Y, Xu X, Yuan F, Cheung TCH, Cheung KW. Integration of image quality and motion cues for face anti-spooﬁng: A neural network approach. Journal of Visual Communication and Image Representation 2016; 38: 451-460.
Xu Z, Li S, Deng W. Learning temporal features using LSTM-CNN architecture for face anti-spooﬁng. 3rd IEEE Asian Conference on Pattern Recognition (ACPR); 2015: 141-145.
Tronci R, Mutoni D, Fadda G, Pili M, Sirena N, Murgia G, Ristori M, Recerche S, Roli F. Fusion of multiple clues for photo-attack detection in face recognition systems. IEEE International Joint Conference on Biometrics (IJCB); 2011: 1-6.
Yang J, Lei Z, Li SZ. Learn convolutional neural network for face anti-spooﬁng. arXiv preprint. Source: áhttps://arxiv.org/abs/1408.5601ñ.
Li L, Feng X, Boulkenafet Z, Xia Z, Li M, Hadid A. An original face anti-spooﬁng approach using partial convolutional neural network. IEEE Image processing theory tools and applications (IPTA) 2016: 1-6.
Video Analysis Technologies. FaceSDK, facial analysis library. Source: áhttps://tevian.ru/product/facesdk/ñ.
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016: 770-778.
Song F, Tan X, Liu X, Chen S. Eyes closeness detection from still images with multi-scale histograms of principal oriented gradients. Pattern Recognition 2014; 47(9): 2825-2838.
Chen T, Li M, Li Y, Lin M, Wang N, Wang M, Xiao T, Xu B, Zhang C, Zhang Z. MXNet: A ﬂexible and efﬁcient machine learning library for heterogeneous distributed systems. arXiv preprint. Source: áhttps://arxiv.org/abs/1512.01274ñ.
Song X, Zhao X, Lin T. Face spooﬁng detection by fusing binocular depth and spatial pyramid coding micro-texture features. IEEE International Conference on Image Processing (ICIP) 2017: 96-100.
Yu C, Jia Y. Anisotropic diffusion-based kernel matrix model for face liveness detection. arXiv preprint. Source: áhttps://arxiv.org/abs/1707.02692ñ.
Alotaibi A, Mahmood A. Deep face liveness detection based on nonlinear diffusion using convolution neural network. Signal, Image and Video Processing 2017; 11(4): 713-720.
Kim W, Suh S, Han JJ. Face liveness detection from a single image via diffusion speed model. IEEE Transactions on Image Processing 2015; 24(8): 2456-2465.
Komulainen J, Hadid A, Pietikinen M, Anjos A, Marcel S. Complementary countermeasures for detecting scenic face spooﬁng attacks. 6th IEEE International Conference on Biometrics (ICB) 2013: 1-7.
Chingovska I, Anjos A, Marcel S. On the effectiveness of local binary patterns in face anti-spooﬁng. IEEE International Conference of the Biometrics Special Interest Group (BIOSIG) 2012: 1-7.
Boulkenafet Z, Komulainen J, Li L, Feng X, Hadid A. OULU-NPU: A mobile face presentation attack database with real-world variations. 12th IEEE International Conference on Automatic Face Gesture Recognition (FG); 2017: 612-618.
Han YJ, Kim W, Park JS. Efﬁcient eye-blinking detection on smartphones: A hybrid approach based on deep learning. Mobile Information Systems 2018; 2018: 6929762.

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