[1]伊力哈木·亚尔买买提.一种新融合算法的维吾尔族人脸识别[J].智能系统学报,2018,13(3):431-436.[doi:10.11992/tis.201710014]
Yilihamu·Yaermaimaiti.A new fusion algorithm for uyghur face recognition[J].CAAI Transactions on Intelligent Systems,2018,13(3):431-436.[doi:10.11992/tis.201710014]
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Yilihamu·Yaermaimaiti.A new fusion algorithm for uyghur face recognition[J].CAAI Transactions on Intelligent Systems,2018,13(3):431-436.[doi:10.11992/tis.201710014]
一种新融合算法的维吾尔族人脸识别
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
13
期数:
2018年第3期
页码:
431-436
栏目:
学术论文—机器感知与模式识别
出版日期:
2018-05-05
- Title:
- A new fusion algorithm for uyghur face recognition
- Keywords:
- face recognition; uyghur; illumination; occlusion; dct; poem; frequency domain state; deep learning
- 分类号:
- TP391.4
- 摘要:
- 针对维吾尔族人脸在光照以及部分遮挡下的辨识率下降和鲁棒性差的问题，提出了二维离散余弦变换（2DDCT）与方向边缘幅值模式（POEM）相融合的维吾尔族人脸识别算法。首先，把维吾尔族人脸图像分块处理，并使用2DDCT把其分块后的维吾尔族人脸图像转换为频域状态；其次，压缩维吾尔族人脸图像以排除维吾尔族人脸图像中无用信息，即中频部分与非低频部分，并进行二维离散余弦逆变换（IDCT）得到重构的维吾尔族人脸图像；然后，经POEM计算维吾尔族人脸图像的特征量得到其相应的POEM直方图并把直方图级联在一起，作为该中心特征点的POEM纹理直方图，得到维吾尔族人脸特征点的纹理特征信息；最后，采用深度学习算法进行分类识别。本文通过实验提出的算法，在自建的维吾尔族人脸库中能够进一步提高其人脸识别率，在维吾尔族人脸数据库中其运算速度也有很大提高。实验结果表明，该算法尤其是在维吾尔族人脸数据库中拥有较好的识别精度，具有很强的鲁棒性，特别是在光照以及部分遮挡下具有很强的优势。
- Abstract:
- 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.
- 参考文献/References:
-
[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.
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备注/Memo
收稿日期:2017-10-23。
基金项目:国家自然科学基金项目（61462082）.
作者简介:伊力哈木·亚尔买买提,男,1978年生,副教授,主要研究方向为图像处理、模式识别。主持参与国家自然科学基金项目5项,发表核心学术论文数十篇。
通讯作者:伊力哈木·亚尔买买提.E-mail:65891080@qq.com.
更新日期/Last Update:
2018-06-25