基于马氏距离的半监督鉴别分析及人脸识别

针对人脸识别应用中人脸样本的类别信息不足以及人脸样本特征间存在相关性的问题,提出了一种基于马氏距离的半监督鉴别分析.该方法在图嵌入框架下利用马氏距离对数据集中带有类别信息的样本进行边界Fisher分析,不仅保持了类内的紧致性和类间的分离性,而且抽取出有利于分类的鉴别特征,同时将不带类别信息的样本用于描述数据集的几何结构,保留了样本间的局部邻域信息.与传统的特征抽取方法相比,该方法有较好的识别性能,在ORL,YALE及AR人脸数据库上的实验验证了该方法的有效性.

Mahalanobis distance-based semi-supervised discriminant analysis for face recognition

To address the problems that there is often no sufficient class-label information of face samples in face recognition application and some relativity also exist among face sample features, a semi-supervised discriminant analysis based on Mahalanobis distance was presented. The method makes use of the Mahalanobis distance to perform marginal fisher analysis (MFA) for labeled samples in the data set, which is on the basis of the graph embedding framework, so that it not only preserves the intraclass compactness and the interclass separability, but also extracts the discriminant characteristics for effective classification, and simultaneously the unlabeled samples were utilized to characterize the geometric structure of the data set, and thus the local neighborhood information among samples was well preserved. Compared with the traditional feature extraction methods, the proposed method has better recognition performance, and the experiments on ORL, YALE and AR face databases demonstrate the effectiveness of this method.