分块双向2DPCA融合局部特征的人脸识别  

Face recognition based on fusion of block bidirectional 2DPCA and local feature

作　　者：杨叶芬[1] 刘海[1] 叶成景[1] 

机构地区：[1]广东科学技术职业学院,广东珠海519090

出　　处：《激光杂志》2015年第1期40-45,共6页Laser Journal

基　　金：广东省自然科学基金资助项目(S2013010012920);广东省高职教育信息技术类专业教学指导委员会项目(XXJS-2013-2004);广东科学技术职业学院教学改革项目(JG201318)

摘　　要：针对(2D)2PCA无法保存某些重要局部特征的问题,提出了一种分块双向二维主成分分析融合局部特征方法。首先,将图像分解为互不重叠的子块,每个子块包含重要的局部信息,利用(2D)2PCA对子块进行特征提取并投影到特征子空间;然后,对每个子块分别设计一个分类器并在一定置信度范围内判别测试样本所属类别。最后,根据所有子块所属类别的置信度之和完成人脸分类。在四个人脸识别数据库上的实验结果表明,相比其它几种人脸识别算法,所提方法取得了更高的识别精度。A fusion method of blocked （2D）2PCA and local feature is proposed because of （2D）2PCA unable to preserve some essential local features. Firstly, the image is decomposed into non-overlapping image sub-blocks;each sub-block contains important local information which uses （2D）2PCA projecting sub-blocks into feature subspace and then design a classifier for each sub-block with a certain degree of confidence voting for sample identification category, the final classification result is the sum of confidence with all the sub-blocks. The proposed method has the same fea-ture matrix as （2D）2PCA. Experiments on four databases show that the proposed method achieves better recognition results in terms of recognition accuracy compared to other more advanced face recognition algorithms.

关 键 词：人脸识别 双向二维主成分分析 特征提取 局部特征 置信度 

分 类 号：TN911[电子电信—通信与信息系统]