Abstract To solve the security problem in identity authentication, the face liveness detection method is always employed. Therefore, a spatial-temporal texture cascaded feature method is proposed to improve the robustness of living face detection. Firstly, local binary pattern(LBP) is utilized to calculate the differential excitation of Weber local descriptor(WLD), and Prewitt operator is exploited to calculate the directional angle of WLD to extract texture features in time domain and space domain. Secondly, the histogram of texture features obtained from three orthogonal space-time planes, XY, XT and YT, is cascaded. Finally, the dynamic texture features, namely spatial-temporal texture cascade features, can be used to determine whether the real face or the disguised face. Experimental results on CASIA face anti-spoofing database and replay-attack database show that the proposed method obtains higher recognition rate than the existing mainstream local texture feature methods and it can be widely used in identity authentication and security monitoring systems.
Fund:Supported by National Natural Science Foundation of China(No.61771347,61072127,61372193), Natural Science Foundation of Guangdong Province(No.S2013010013311,10152902001000
About author:: (GAN Junying, Ph.D., professor. Her research interests include biometric identification.) (ZHAI Yikui(Corresponding author), Ph.D., associate professor. His research interests include biometric identification and SAR image recognition.) (XIANG Li, master student. Her research interests include biometric identification.) (CAO He, master student. Her research interests include biometric identification and face beauty prediction.) (HE Guohui, Ph.D., professor. His research interests include image processing, virtual reality and multimedia information system.) (ZENG Junying, Ph.D., associate profe-ssor. His research interests include machine vision and biometrics identification.)(TAN Haiying, master student. Her research interests include biometric identification and face beauty prediction.) (DENG Wenbo, master student. His research interests include biometric identification and SAR image recognition.)
[1] KOSE N, DUGELAY J L.On the Vulnerability of Face Recognition Systems to Spoofing Mask Attacks // Proc of the IEEE International Conference on Acoustics, Speech and Signal Processing. Washington, USA: IEEE, 2013: 2357-2361. [2] 曹瑜,涂玲,毋立芳.身份认证中灰度共生矩阵和小波分析的活体人脸检测算法.信号处理, 2014, 30(7): 830-835. (CAO Y, TU L, WU L F.Face Liveness Detection Using Gray Level Co-occurrence Matrix and Wavelets Analysis in Identity Authentication. Journal of Signal Processing, 2014, 30(7): 830-835.) [3] 李冰,王宝亮,由磊,等.应用并联卷积神经网络的人脸防欺骗方法.小型微型计算机系统, 2017, 38(10): 2187-2191. (LI B, WANG B L, YOU L, et al. Face Anti-spoofing Algorithm Applying with Parallel Convolutional Neural Network. Journal of Chinese Computer Systems, 2017, 38(10): 2187-2191.) [4] KIM G, EUM S, SUHR J K, et al. Face Liveness Detection Based on Texture and Frequency Analyses // Proc of the 5th IAPR International Conference on Biometrics. Washington, USA: IEEE, 2012:67-72. [5] PAN G, SUN L, WU Z H, et al. Eyeblink-Based Anti-spoofing in Face Recognition from a Generic Web Camera // Proc of the 11th IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2007. DOI: 10.1109/ICCV.2007.4409068. [6] ZHAO G Y, PIETIKAINEN M.Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(6): 915-928. [7] WEN D, HAN H, JAIN A K.Face Spoof Detection with Image Distortion Analysis. IEEE Transactions on Information Forensics and Security, 2015, 10(4): 746-761. [8] BASHIER H K, HOE L S, HUI L T, et al. Texture Classification via Extended Local Graph Structure. Optik, 2016, 127(2): 638-643. [9] PEREIRA T D F, ANJOS A, DE MARTINO J M, et al. LBP-TOP Based Countermeasure Against Face Spoofing Attacks // Proc of the Asian Conference on Computer Vision. Berlin, Germany: Springer, 2012: 121-132. [10] PEREIRA T D F, KOMULAINEN J, ANJOS A, et al. Face Liveness Detection Using Dynamic Texture. EURASIP Journal on Image and Video Processing, 2014. DOI:10.1186/1687-5281-2014-2. [11] CHEN J, SHAN S G, HE C, et al. WLD: A Robust Local Image Descriptor. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1705-1720. [12] LIU F, TANG Z M, TANG J H.WLBP: Weber Local Binary Pa-ttern for Local Image Description. Neurocomputing, 2013, 120: 325-335. [13] MEI L, YANG D K, FENG Z X, et al. WLD-TOP Based Algorithm Against Face Spoofing Attacks // Proc of the Chinese Confe-rence on Biometric Recognition. Berlin, Germany: Springer, 2015: 135-142. [14] ZHANG Z W, YAN J J, LIU S F, et al. A Face Anti-spoofing Database with Diverse Attacks // Proc of the International Conference on Biometrics. Washington, USA: IEEE, 2012: 26-31. [15] ANJOS A, KOMULAINEN J, MARCEL S, et al. Face Anti-spoofing: Visual Approach // MARCEL S, NIXON M S, LI S Z, eds. Handbook of Biometric Anti-spoofing. London, UK: Springer, 2014: 65-82. [16] OJANSIVU V, HEIKKILA J.Blur Insensitive Texture Classification Using Local Phase Quantization // Proc of the 3rd International Conference on Image and Signal Processing. Berlin, Germany: Springer, 2008: 236-243. [17] OJALA T, PIETIKAINEN M, MAEMPAA T.Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987. [18] BOULKENAFET Z, KOMULAINEN J, FEBG X Y, et al. Scale Space Texture Analysis for Face Anti-spoofings // Proc of the International Conference on Biometrics. Washington, USA: IEEE, 2016. DOI: 10.1109/ICB.2016.7550078. [19] AKBULUT Y, ŞENGUR A, BUDAK Ü, et al. Deep Learning Based Face Liveness Detection in Videos // Proc of the Artificial Intelligence and Data Processing Symposium. Washington, USA: IEEE, 2017: 1-4. [20] GAKBALLY J, MARCEL S.Face Anti-spoofing Based on General Image Quality Assessment // Proc of the 22nd IEEE International Conference on Pattern Recognition. Washington, USA: IEEE, 2014: 1173-1178. [21] 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.
2019, Vol. 32 
