【摘要】 提出了一种基于核技术的融合了反转Fisher鉴别准则和正交化技术的KIOFD(Kernel Inverse Orthogonalized Fisher Dis-criminant)算法,并把这一算法应用于人脸识别中。线性人脸识别中存在两个突出问题:(1)在光照、表情、姿态变化较大时,人脸图像分类是复杂的、非线性的;(2)小样本问题,即当训练样本数量小于样本特征空间维数时,导致类内散布矩阵奇异。对于第1个问题,可以采用核技术提取人脸图像样本的非线性特征,对于第2个问题,采用了反转Fisher鉴别准则和正交化结合的算法。通过对ORL、Yale GroupB以及UMIST3个人脸库的实验表明,提出的算法是可行的、高效的。更多还原

【Abstract】 This paper proposes a new algorithm,named Kernel Inverse Orthogonalized Fisher Discriminant(KIOFD),to extract optimal discriminant feature,and applies this method to face recognition.There are two problems in linear face recognition:the first one is that the distribution of face images with different pose,illumination and face expression is complex and nonlinear.The second one is the Small Sample Size(S3) problem.This problem occurs when the number of training samples is smaller than the dimensionality of feature vector,which results in a sigular within-class scatter matrix.For the former,kernel technique can be used to extract nonlinear feature,and for the latter,an inverse fisher discriminant criteria combined with orthogonalized technique is introduced to overcome S3 problem.Three databases,namely ORL,Yale Group B,and UMIST are selected for evalution.The results are encouraging.更多还原