Local uncorrelated local discriminant embedding for face recognition

作     者：Xiao-hu MA Meng YANG Zhao ZHANG 

作者机构：School of Computer Science and TechnologySoochow University State Key Laboratory for Novel Software TechnologyNanjing University 

出 版 物：《Frontiers of Information Technology & Electronic Engineering》 (信息与电子工程前沿（英文版）)

年 卷 期：2016年第17卷第3期

页      面：212-223页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0839[工学-网络空间安全] 080203[工学-机械设计及理论] 0802[工学-机械工程] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Project supported by the National Natural Science Foundation of China(No.61402310) the Natural Science Foundation of Jiangsu Province,China(No.BK20141195) the State Key Laboratory for Novel Software Technology Foundation of Nanjing University,China(No.KFKT2014B11) 

主　　题：量子细胞自动机 可逆电路 传统设计 奇偶校验 发生器 校验器 元胞自动机 低功率 

摘      要：The feature extraction algorithm plays an important role in face recognition. However, the extracted features also have overlapping discriminant information. A property of the statistical uncorrelated criterion is that it eliminates the redundancy among the extracted discriminant features, while many algorithms generally ignore this property. In this paper, we introduce a novel feature extraction method called local uncorrelated local discriminant embedding(LULDE). The proposed approach can be seen as an extension of a local discriminant embedding(LDE)framework in three ways. First, a new local statistical uncorrelated criterion is proposed, which effectively captures the local information of interclass and intraclass. Second, we reconstruct the affinity matrices of an intrinsic graph and a penalty graph, which are mentioned in LDE to enhance the discriminant property. Finally, it overcomes the small-sample-size problem without using principal component analysis to preprocess the original data, which avoids losing some discriminant information. Experimental results on Yale, ORL, Extended Yale B, and FERET databases demonstrate that LULDE outperforms LDE and other representative uncorrelated feature extraction methods.