One Sample per Person Face Recognition Based on Deep Autoencoder
ZHANG Yan, PENG Hua
1.School of Information Systems Engineering, Information Engineering University, Zhengzhou 450000 2.School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002
Abstract：Since there is only one sample for each subject, it is hard to describe intra-class variations of the subject. The performance of state-of-the-art face recognition algorithms declines in one sample per person(OSPP) face recognition. In this paper, an OSPP face recognition algorithm based on deep autoencoder(OSPP-DA) is proposed. In OSPP-DA, deep autoencoder is trained by all the images in the gallery firstly, and a generalized deep autoencoder(GDA) is generated. Then, the GDA is fine-tuned by the single sample of the subject, and a class-specified deep autoencoder(CDA) is obtained. For classification, query images are input to CDAs and the reconstruction samples of the corresponding subjects have the same intra-class variation as query images. A Softmax regression model is trained by the reconstruction samples and the query images are identified by the Softmax regression model. Experiments on public testing database are conducted and the results show the validity of OSPP-DA. Compared with some state-of-the-art algorithms, the proposed algorithm produces better performance with less time.
张彦， 彭华. 基于深度自编码器的单样本人脸识别*[J]. 模式识别与人工智能, 2017, 30(4): 343-352.
ZHANG Yan, PENG Hua. One Sample per Person Face Recognition Based on Deep Autoencoder. , 2017, 30(4): 343-352.
[1] TAN X Y , CHEN S C , ZHOU Z H, et al. Face Recognition from a Single Image per Person: A Survey. Pattern Recognition, 2006, 39(9): 1725-1745. [2] ETEMAD K, CHELLAPPA R. Discriminant Analysis for Recognition of Human Face Images. Journal of the Optical Society of America A, 1997, 14(8): 1724-1733. [3] ROWEIS S T, SAUL L K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 2000, 290(5500): 2323-2326. [4] HE X F, NIYOGI P. Locality Preserving Projections // THRUN S, SAUL L K, SCHLKOPF P B, eds. Advances in Neural Information Processing Systems 16. Cambridge, USA: The MIT Press, 2003: 153-160. [5] WRIGHT J, YANG A Y, GANESH A, et al. Robust Face Recognition via Sparse Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227. [6] YANG J, ZHANG D, FRANGI A F, et al. Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(1): 131-137. [7] DENG W H, HU J N, GUO J, et al. Robust, Accurate and Efficient Face Recognition from a Single Training Image: A Uniform Pursuit Approach. Pattern Recognition, 2010, 43(5): 1748-1762. [8] LU J W, TAN Y P, WANG G. Discriminative Multimanifold Analysis for Face Recognition from a Single Training Sample per Person. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(1): 39-51. [9] LIU F, TAN J H, SONG Y, et al. Local Structure Based Sparse Representation for Face Recognition with Single Sample per Person // Proc of the IEEE International Conference on Image Processing, Washington, USA: IEEE, 2014: 713-717. [10] WANG B, LI W F, LI Z M, et al. Adaptive Linear Regression for Single-Sample Face Recognition. Neurocomputing, 2013, 115: 186-191. [11] HUANG D A, WANG Y C F. With One Look: Robust Face Re- cognition Using Single Sample per Person // Proc of the 21st ACM International Conference on Multimedia. New York, UWA: ACM, 2013: 601-604. [12] DENG W H, HU J N, GUO J. Extended SRC: Undersampled Face Recognition via Intraclass Variant Dictionary. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(9): 1864-1870. [13] DENG W H, HU J N, GUO J. In Defense of Sparsity Based Face Recognition // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2013: 399-406. [14] CHEN S B, DING C H Q, LUO B. Extended Linear Regression for Undersampled Face Recognition. Journal of Visual Communication and Image Representation, 2014, 25(7): 1800-1809. [15] JI H K, SUN Q S, JI Z X, et al. Collaborative Probabilistic Labels for Face Recognition from Single Sample per Person. Pattern Re- cognition, 2017, 62: 125-134. [16] HINTON G E, SALAKHUTDINOV R R. Reducing the Dimensionality of Data with Neural Networks. Science, 2006, 313(5786): 504-507. [17] KHAN S H, BENNAMOUN M, SOHEL F, et al. Automatic Sha- dow Detection and Removal from a Single Image. IEEE Transactions on Pattern analysis and Machine Intelligence, 2016, 38(3): 431-446. [18] DONG C, LOY C C, HE K M, et al. Image Super-Resolution Using Deep Convolutional Networks.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2): 295-307. [19] ZHAO R, OUYANG W L, LI H S, et al. Saliency Detection by Multi-context Deep Learning // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2015: 1265-1274. [20] SUN Y, WANG X G, TANG X O. Deep Learning Face Representation from Predicting 10000 Classes // Proc of the IEEE Confe- rence on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2014: 1891-1898. [21] SUN Y, WANG X G, TANG X O. Deeply Learned Face Representations Are Sparse, Selective, and Robust // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2015: 2892-2900. [22] SCHROFF F, KALENICTBHENKO D, PHILBIN J. FaceNet: A Unified Embedding for Face Recognition and Clustering // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2015: 815-823. [23] SUN Y, CHEN Y H, WANG X G, et al. Deep Learning Face Representation by Joint Identification Verification // Proc of the 27th International Conference on Neural Information Processing Systems. Cambridge, USA: The MIT Press, 2014: 1988-1996. [24] GAO S H, ZHANG Y T, JIA K, et al. Single Sample Face Recognition via Learning Deep Supervised Autoencoders. IEEE Transactions on Information Forensics and Security, 2015, 10(10): 2108-2118. [25] ARRIBAS J I, CID-SUEIRO J, ADALI T, et al. Estimates of Constrained Multi-class a Posteriori Probabilities in Time Series Problems with Neural Networks // Proc of the International Joint Con- ference on Neural Networks. Washington, USA: IEEE, 1999:1560-1561. [26] HINTON G E. A Practical Guide to Training Restricted Boltzmann Machines. 2nd Edition. Berlin, Germany: Springer, 2012. [27] RUMELHART D E, HINTON G E, WILLIAMS R J. Learning Representations by Back-Propagating Errors. Nature,1986,323(6088): 533-536. [28] NIELSEN M A. Neural Networks and Deep Learning[M/OL]. [2015-04-08]. http://neuralnetworksanddeeplearning.com. [29] NG A, NGIAM J Q, FOO C Y. Data Preprocessing[EB/OL].[2015-05-15]. http://ufldl.stanford.edu/wiki/index.php/Data_Preprocessing. [30] NG A, NGIAM J Q, FOO C Y. Whitening[EB/OL]. [2015-05-20]. http://ufldl.stanford.edu/wiki/index.php/Whitening. [31] ERHAN D, BENGIO Y, COURVILLE A, et al. Visualizing Higher-Layer Features of a Deep Network. Technical Report, 1341. Montreal, Canada: University of Montreal, 2009.
2017, Vol. 30 
