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 Yilihamu·Yaermaimaiti.A new fusion algorithm for uyghur face recognition[J].CAAI Transactions on Intelligent Systems,2018,13(03):431-436.[doi:10.11992/tis.201710014]





A new fusion algorithm for uyghur face recognition
新疆大学 电气工程学院, 新疆 乌鲁木齐 830047
College of Electncian Engineering, Xinjiang University, Urumqi 830047, China
face recognitionuyghurilluminationocclusiondctpoemfrequency domain statedeep learning
Considering the inferior robustness of Uyghur face recognition under illumination and partial occlusion, this study proposes a Uyghur face recognition algorithm based on two-dimensional discrete cosine transform (2DDCT) and patterns of oriented edge magnitudes (POEM). The Uygur face images were partitioned into several blocks, and 2DDCT was used to transform the partitioned images into a frequency domain. The images were compacted and irrelevant information was excluded, i.e., the medium-frequency portion and the low-frequency portion, and then a two-dimensional inverse discrete cosine transform (IDCT) was carried out to obtain a reconstructed Uygur face image. The POEM was then used to calculate the characteristic quantity of the Uygur face image to obtain the corresponding POEM histogram. All histograms were cascaded together as the POEM texture histogram of the central characteristic point to acquire the texture feature information of Uygur face feature point. Finally, a deep learning algorithm was used to classify recognition. The algorithm proposed in this paper can improve the face recognition rate and operation speed of a self-built Uyghur face database. Experimental results show that the algorithm has good recognition accuracy, especially for a Uyghur face database, and strong robustness, especially under illumination and partial occlusion.


[1] LU Jiwen, WANG Gang, DENG Jie. Simultaneous feature and dictionary learning for image set based face recognition[J]. IEEE transactions on image processing, 2017, 26(8):4042-4054.
[2] AHONEN T, HADID A, PIETIKAINEN M. Face description with local binary patterns:application to face recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2006, 28(12):2037-2041.
[3] 周汐, 曹林. 分块LBP的素描人脸识别[J]. 中国图象图形学报, 2015, 20(1):50-58. ZHOU Xi, CAO Lin. The sketch face recognition combining with AdaBoost and blocking LBP[J]. Journal of image and graphics, 2015, 20(1):50-58.
[4] VU N S, DEE H M, CAPLIER A. Face recognition using the POEM descriptor[J]. Pattern recognition, 2012, 45(7):2478-2488.
[5] ZHANG Haiyang. Face recognition based on DCT and PCA[M]//WAN Xiaofeng. Electrical Power Systems and Computers. Berlin, Heidelberg, 2011:451-455.
[6] SONG Haifeng, CHEN Guangsheng, WEI Hairong, et al. The improved (2D)2 PCA algorithm and its parallel implementation based on image block[J]. Microprocessors and microsystems, 2016, 47:170-177.
[7] BHAT F A, WANI M A. Gabor wavelet based face recognition under varying lighting, pose and expression conditions[C]//Proceedings of the 2nd International Conference on Computing for Sustainable Global Development. New Delhi, Indi, 2015:1314-1318.
[8] GAO Xiaojing, XUE Heru, PAN Xin, et al. Mongolia nationality face recognition based on G(2D)2PCA and SVM Classification[C]//Proceedings of the 4th International Conference on Information Science and Control Engineering. Changsha, China, 2017:461-465.)
[9] 殷俊, 孙仕亮. 基于最近正交矩阵的二维鉴别投影及人脸识别应用[J]. 计算机辅助设计与图形学学报, 2017, 29(8):1457-1464. YIN Jun, SUN Shiliang. Two dimensional discriminative projection based on nearest orthogonal matrix and its application to face recognition[J]. Journal of computer-aided design & computer graphics, 2017, 29(8):1457-1464.
[10] DENG Weihong, HU Jiani, LU Jiwen, et al. Transform-invariant PCA:A unified approach to fully automatic faceAlignment, representation, and recognition[J]. IEEE Transactions on pattern analysis and machine intelligence, 2014, 36(6):1275-1284.
[11] CHEN Yefei, SU Jianbo. Sparse embedded dictionary learning on face recognition[J]. Pattern recognition, 2017, 64:51-59.
[12] 何林巍, 黄福珍. 基于POEM_SLPP的人脸识别算法[J]. 计算机应用研究, 2017, 34(6):1896-1899. HE Linwei, HUANG Fuzhen. Face recognition algorithm based on POEM_SLPP[J]. Application research of computers, 2017, 34(6):1896-1899.
[13] CHEN Zhanwei, HUANG Wei, LV Zhihan. Towards a face recognition method based on uncorrelated discriminant sparse preserving projection[J]. Multimedia tools and applications, 2017, 76(17):17669-17683.
[14] Lu J, Liong V E, Wang G, et al. Joint feature learning for face recognition[J]. IEEE transactions on information forensics and security, 2017, 10(7):1371-1383.
[15] GUO Shanshan, CHEN Shiyu, LI Yanjie. Face recognition based on convolutional neural network and support vector machine[C]//Proceedings of IEEE International Conference on Information and Automation. Ningbo, China, 2017:1787-1792.
[16] DING Changxing, CHOI J, TAO Dacheng, et al. Multi-directional multi-level dual-cross patterns for robust face recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2016, 38(3):518-531.
[17] DA MARSICO M, NAPPI M, RICCIO D, et al. Robust face recognition for uncontrolled pose and illumination changes[J]. IEEE transactions on systems, man, and cybernetics:systems, 2012, 43(1):149-163.
[18] KUSUMA G P, CHUA C S. PCA-based image recombination for multimodal 2D +3D face recognition[J]. Image and vision computing, 2011, 29(5):306-316.
[19] BENGHERABI M, MEZAI L, HARIZI F. 2DPCA-based techniques in DCT domain for face recognition[J]. International journal of intelligent systems technologies and applications, 2009, 7(3):243-265.
[20] FANG Bingwu, HUANG Zhiqiu, LI Yong, et al. υ-Support vector machine based on discriminant sparse neighborhood preserving embedding[J]. Pattern analysis and applications, 2017, 20(4):1077-1089.
[21] BENGIO Y. Learning deep architectures for AI[J]. Foundations and trends in machine learning, 2009, 2(1):1-127.


 JIN Yi,RUAN Qiu-qi.A partially weighted twodimensional PCA for face recognition[J].CAAI Transactions on Intelligent Systems,2007,2(03):25.
[2]任小龙,苏光大,相 燕.使用第2代身份证的人脸识别身份认证系统[J].智能系统学报,2009,4(03):213.
 REN Xiao-long,SU Guang-da,XIANG Yan.Face authentication system using the Chinese second generation identity card[J].CAAI Transactions on Intelligent Systems,2009,4(03):213.
 SU Guang-da.Face recognition system designed to integrate multiple techniques[J].CAAI Transactions on Intelligent Systems,2009,4(03):471.[doi:doi:10.3969/j.issn.1673-4785.2009.06.001]
 WANG Kejun,ZOU Guofeng,ZHANG Jie.Analysis of the influence of SPCA parameters on the recognition of a single sample face[J].CAAI Transactions on Intelligent Systems,2011,6(03):531.
 DU Jixiang,ZHAI Chuanmin,YE Yongqing.An agespan face recognition method based on an NMF algorithm with sparseness constraints[J].CAAI Transactions on Intelligent Systems,2012,7(03):271.
 RUAN Xiaohu,LI Weijun,QIN Hong,et al.An assessment method for face alignment based on feature matching[J].CAAI Transactions on Intelligent Systems,2015,10(03):12.[doi:10.3969/j.issn.1673-4785.201312064]
 SUN Jinguang,MENG Fanyu.Face recognition by weighted fusion of facial features[J].CAAI Transactions on Intelligent Systems,2015,10(03):912.[doi:10.11992/tis.201509025]
 MA Xiao,ZHANG Fandong,FENG Jufu.Sparse representation via deep learning features based face recognition method[J].CAAI Transactions on Intelligent Systems,2016,11(03):279.[doi:10.11992/tis.201603026]
 LIU Xunli,GONG Xun,WANG Guoyin.Face recognition based on the linear representation model without residual estimation[J].CAAI Transactions on Intelligent Systems,2014,9(03):285.[doi:10.3969/j.issn.1673-4785.201309065]
 XIA Yangyang,GONG Xun,HONG Xijin.Research on the data cleansing problem for face recognition technology[J].CAAI Transactions on Intelligent Systems,2017,12(03):616.[doi:10.11992/tis.201706025]


更新日期/Last Update: 2018-06-25