G-LBP和方差投影交叉熵的人脸识别  

G-LBP and Variance Cross Projection Function for Face Recognition

作　　者：胡敏[1] 余子玺 王晓华[1] 任福继[1,2] 何蕾[3] HU Min;YU Zixi;WANG Xiaohua;REN Fuji;HE Lei(Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, School of Computer and Information,Hefei University of Technology, Hefei Anhui 230009, China;Graduate School of Advanced Technology and Science, University of Tokushima, Tokushima 770-8502, Japan;School of Mathematics and Information, Hefei University of Technology, Hefei Anhui 230009, China)

机构地区：[1]合肥工业大学计算机与信息学院情感计算与先进智能机器安徽省重点实验室,安徽合肥230009 [2]德岛大学先端技术科学教育部 [3]合肥工业大学理学院,安徽合肥230009

出　　处：《图学学报》2017年第1期82-89,共8页Journal of Graphics

基　　金：国家自然科学基金项目(61432004;61672202);国家自然科学青年基金项目(61300119;61502141);安徽省自然科学基金项目(1408085MKL16;1508085QF128)

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.

关 键 词：人脸识别 方差投影熵 G-LBP BP神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]