基于MBC和POEM特征的人脸识别方法被引量：1

Face Recognition Algorithm Based on MBC and POEM Feature

下载PDF

导出

摘要针对人脸识别中单一特征难以取得理想效果的问题,提出了基于MBC和POEM特征融合的人脸识别方法.首先,在归一化的人脸图像上提取MBC编码图和POEM编码图,在每个编码图块上生成特征向量,应用线性判别分析对特征向量进行低维映射,并对其进行赋权相加得到最终相似度.所提算法在FERET的Dup1,Dup2,Fb和Fc 4个测试库上取得了较高的识别率,分别为93.77%,90.60%,99.58%和99.49%;在误识率为0.1%的条件下,在4个测试库上的认证率分别为95.70%,92.31%,99.75%和100%,进一步验证了该方法的有效性. Due to the representation difficulty of a face image by a single type feature used in face recognition,a MBC feature and POEMfeature-based face recognition scheme was proposed.Firstly,MBC and POEMcoding patterns were extracted from normalized face images. Secondly,feature vector of every block was generated by dividing the MBC and POEMcoding patterns into blocks. Finally,the classification capacity of features was enhanced by using weighted piecewise LDA algorithm. Recognition and verification test were carried out using the proposed algorithm on Dup1,Dup2,Fb and Fc,respectively,which were the four subsets of FERET. The recognition rates were 93. 77%,90. 60%,99. 58%,and 99. 49%,respectively,and the verification rates（ false accepted rate is 0. 1%） were 95. 70%,92. 31%,99. 75%,and 100%,respectively. All these results indicated the effectiveness of the proposed algorithm.

作者张祥德朱和贵李倩颖唐青松

机构地区东北大学理学院

出处《东北大学学报（自然科学版）》 EI CAS CSCD 北大核心 2015年第11期1526-1529,共4页 Journal of Northeastern University(Natural Science)

基金国家自然科学基金资助项目(61202085) 辽宁省自然科学基金资助项目(201202074) 中央高校基本科研业务费专项资金资助项目(N140503004)

关键词人脸识别 MBC特征 POEM特征特征融合赋权分段线性判别分析 face recognition MBC feature POEM feature feature fusion weighted piecewise LDA

分类号 TP391.41 [自动化与计算机技术—计算机应用技术]

引文网络
相关文献

参考文献10

1Tan X Y, Triggs B. Enhanced local texture feature sets for face recognition under difficult lighting conditions [ J]. IEEE Transactions on Image Processing, 2010, 19 ( 6 ) : 1635 - 1650.
2Yang M,Zhang L, Shiu S C K, et al. Monogenic binary coding:an efficient local feature extraction approach to face recognition[ J]. IEEE Transactions on Information Forensics and Security, 2012, 7(6) :1738 - 1751.
3Felsberg M, Sommer G. The monogenic signal [ J ]. IEEE Transactions on Signal Processing, 2001, 49 ( 12 ) : 3136 - 3144.
4Vu N S. Exploring patterns of gradient orientations and magnitudes for face recognition [ J ]. IEEE Transactions on Information Forensics and Security ,2013,8(2) :295 -304.
5Vu N S, Caplier A. Face recognition with patterns of oriented edge magnitudes [ C ]//Computer Vision-ECCV 2010. Berlin: Springer- Heidelberg,2010: 313 - 326.
6Zhang W, Shan S, Gao W, et al. Local Gabor binary pattern histogram sequence ( LGBPHS ) : a novel non-statistical model for face representation and recognition [ C ]//10th IEEE International Conference on Computer Vision 2005. New York:I EEE,2005:786 -791.
7Zhang B, Shan S, Chen X. Histogram of Gabor phase patterns (HGPP): a novel object representation approach for face recognition [ J ]. 1EEE Transactions on Image Processing, 2007,16( 1 ) :57 -68.
8Vu N S, Caplier A. Enhanced patterns of oriented edge magnitudes for face recognition and image matching [ J ]. IEEE Transactions on Image Processing, 2012, 21 ( 3 ): 1352 - 1365.
9Zou J, Ji Q, Nagy G. A comparative study of local matching approach for face recognition [ J ]. 1EEE Transactions on Image Processing ,2007,16 (10) :2617 - 2628.
10Tan X, Triggs B. Fusing Gabor and LBP feature sets for kernel-based face recognition [ C]//10th IEEE International Conference on Computer Vision 2005. New York: IEEE, 2005,786 - 791.