Weights learning algorithm of facial local feature based on scatter criterion
Zongying Ou · Ha'erbin gongye daxue xuebao · 2006
This paper proposes a method for computing local facial feature weights based on scatter criterion.In the face recognition algorithm by using local features,different weights can be assigned to different local features according to their contributions to face recognition in order to increase the recognition performance.From the point of pattern recognition,those local features which can be easily classified contribute more to face recognition and vice versa.In the feature space,when the distributions of with-in classes are close and the distances between different classes are far,pattern features can be classified easily.The trace of scatter matrix describes the scatter degree of pattern features.By the means of statistical learning,the trace of the product of the inversion matrix of the within-class scatter matrix and between-class scatter matrix is used as a criterion for weighing the discriminative capability of a local feature,and the weight of local features are determined based on their discriminative capability.Experimental results show that the weights which are computed by scatter criterion can increase the recognition performance significantly,which confirm the feasibility of our algorithm.Compared with similar algorithms,this algorithm has the property of computing simply and being realized easily.