G-LBP和方差投影交叉熵的人脸识别
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摘要
针对基于Gabor特征识别人脸时存在数据维数大及冗余等问题,将变换后的频域特征转换到空间域,提出一种新的特征描述算法G-LBP。为了进一步提高系统的稳定性及精度,丰富人脸描述特征,从熵值角度对人脸进行补充描述。针对方差投影熵在特征描述上,忽略了行列之间的交互信息,定义了方差交叉投影熵。最后,基于BP神经网络对两种不同的特征空间进行决策层加权融合完成人脸识别。实验结果表明,G-LBP特征提取方法降低了数据间的冗余,且能保留有效地判别信息;方差投影熵和方差交叉投影熵丰富了人脸特征的描述;决策层加权融合的方法较好地发挥分类器间的集成作用,最终有效地提高了人脸的识别率,与其他文献的算法相比,也证明了该方法的有效性。
In order to enhance robustness of traditional Gabor features towards illumination, expression and pose variance and overcome its high dimension problem, the paper proposes a face recognition method based on Gabor, local binary patter and variance projection entropy improved algorithm.First, the multi direction multi-scale fusion Gabor image is coded with LBP, and the coded image fused and the histograms of image block calculated.Second, a local projection entropy feature extraction is adopted for face images with anti-geometric distortion variance projection entropy and cross variance projection entropy operator.Finally, the face recognition is completed by using BP neutral network to fuse and make decision weightily.The G-LBP feature extraction reduces the redundancy of data greatly, and maintains the integrity of the effective information.Variance projection of entropy and cross entropy improves the richness of the feature.The weighted fusion in decision-making layer plays an important role of integration between the classifiers and improves the recognition rate of face recognition.Compared with other literature algorithms, experiment results verify the effectiveness and superiority of the proposed algorithm.
In order to enhance robustness of traditional Gabor features towards illumination, expression and pose variance and overcome its high dimension problem, the paper proposes a face recognition method based on Gabor, local binary patter and variance projection entropy improved algorithm.First, the multi direction multi-scale fusion Gabor image is coded with LBP, and the coded image fused and the histograms of image block calculated.Second, a local projection entropy feature extraction is adopted for face images with anti-geometric distortion variance projection entropy and cross variance projection entropy operator.Finally, the face recognition is completed by using BP neutral network to fuse and make decision weightily.The G-LBP feature extraction reduces the redundancy of data greatly, and maintains the integrity of the effective information.Variance projection of entropy and cross entropy improves the richness of the feature.The weighted fusion in decision-making layer plays an important role of integration between the classifiers and improves the recognition rate of face recognition.Compared with other literature algorithms, experiment results verify the effectiveness and superiority of the proposed algorithm.
引文
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[24]Márdero S,SCHMOOK B,CHRISTMAN Z,et al.Face recognition using adaptive margin fisher’s criterion and linear discriminant analysis[J].International Arab Journal of Information Technology,2014,11(2):149-158.
[2]VISHWAKARMA V P.Illumination normalization using fuzzy filter in DCT domain for face recognition[J].International Journal of Machine Learning&Cybernetics,2015,6(1):17-34.
[3]JIANG X D,LAI J.Sparse and dense hybrid representation via dictionary decomposition for face recognition[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2015,37(5):1067-1079.
[4]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[J].信号处理,2014,30(7):830-835.
[5]李雨龙,管业鹏.基于人脸朝向的非穿戴自然人机交互[J].电子学报,2015(8):1583-1588.
[6]CHEON Y,KIM D.Natural facial expression recognition using differential-AAM and manifold learning[J].Pattern Recognition,2009,42(7):1340-1350.
[7]PERLIBAKAS V.Distance measures for PCA-based face recognition[J].Pattern Recognition Letters,2004,25(6):711-724.
[8]EDIZKAN R,?EVIKALP H,YAVUZ H S.DCV-based face recognition system and its application on the embedded development board[J].Global Journal on Technology,2013,4(2):734-738.
[9]ABDULLAH M F A,SAYEED M S,MUTHU K S,et al.Face recognition with symmetric local graph structure(SLGS)[J].Expert Systems with Applications,2014,41(14):6131-6137.
[10]CHAKRABORTI T,CHATTERJEE A.A novel binary adaptive weight GSA based feature selection for face recognition using local gradient patterns,modified census transform,and local binary patterns[J].Engineering Applications of Artificial Intelligence,2014,33(1):80-90.
[11]ZHU N,TANG T,TANG S,et al.A sparse representation method based on kernel and virtual samples for face recognition[J].Optik-International Journal for Light and Electron Optics,2013,124(124):6236-6241.
[12]HUANG Z H,LI W J,WANG J,et al.Face recognition based on pixel-level and feature-level fusion of the top-level’s wavelet sub-bands[J].Information Fusion,2015,22:95-104.
[13]ZHOU L,LIU W,LU Z M,et al.Face recognition based on curvelets and local binary pattern features via using local property preservation[J].Journal of Systems&Software,2014,95(9):209-216.
[14]胡敏,朱弘,王晓华,等.基于梯度Gabor直方图特征的表情识别方法[J].计算机辅助设计与图形学学报,2013,25(12):1856-1861.
[15]刘帅师,田彦涛,万川.基于Gabor多方向特征融合与分块直方图的人脸表情识别方法[J].自动化学报,2011,37(12):1455-1463.
[16]ZHANG B C,GAO Y S,ZHAO S Q,et al.Local derivative pattern versus local binary pattern:face recognition with high-order local pattern descriptor[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2010,19(2):533-544.
[17]ZHANG 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]//Tenth IEEE International Conference on Computer Vision.Los Alamitos:IEEE Computer Society Prees,2005:786-791.
[18]TAN X,TRIGGS B.Fusing Gabor and LBP feature sets for kernel-based face recognition[C]//International Workshop on Analysis and Modeling of Faces and Gestures.Berlin:Springer,2007:235-249.
[19]苏秀琴,梁金峰.一种基于单元信息熵的目标匹配改进算法[J].光子学报,2009,38(11):3040-3043.
[20]周军妮,王燕妮,魏蕊.基于交叉投影熵的车辆目标匹配算法[J].电视技术,2012,36(23):160-164.
[21]邓松,王汝传.一种基于网格服务的分布式GEP-BP分类算法[J].电子学报,2009,37(11):2600-2603.
[22]FENG G C,YUEN P C.Variance projection function and its application to eye detection for human face recognition[J].Pattern Recognition Letters,1998,19(9):899-906.
[23]周军妮,杨润玲,王燕妮,等.一种结合交叉熵和投影特征的图像匹配算法[J].小型微型计算机系统,2013,34(2):405-408.
[24]Márdero S,SCHMOOK B,CHRISTMAN Z,et al.Face recognition using adaptive margin fisher’s criterion and linear discriminant analysis[J].International Arab Journal of Information Technology,2014,11(2):149-158.