基于多类最大散度差的人脸表示方法

Face Representation Based on the Multiple-class Maximum Scatter Difference

将用于两类分类的最大散度差鉴别准则推广为多类最大散度差鉴别准则,并建立了基于该准则的一种新的人脸表示方法．基于多类最大散度差鉴别准则的人脸表示方法有效避免了传统鉴别分析方法在人脸特征提取时通常面临的小样本模式识别问题．在国际标准人脸图像数据库ORL、Yale以及FERET上的实验结果表明,与Fisherfaces、Eigenfaces、正交补空间、零空间等人脸特征提取方法相比,新的人脸表示方法具有一定的优势．

In this paper we extend the maximum scatter difference discriminant criterion which is proposed for binary classification to the multiple-class maximum scatter difference discriminant criterion. Based on this new criterion we establish a novel face representation method. The facial feature extraction method based on the multiple-class maximum scatter difference discriminant criterion effectively avoids the small sample size problem which always brings troubles to conventional discriminant analysis methods when they are applied to face recognition tasks. Experimental results conducted on international benchmark datasets such as ORL, Yale, and FERET face image databases demonstrate that the novel face representation method is promising in comparison with Fisherfaces, eigenfaces, orthogonal complimentary space method, and null space method.