A semi-supervised coefficient selection method for face recognition
Rubo Zhang · Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University · 2012
In face recognition,there exist the problems of high dimensionality of image data and requiring many class labels.To overcome these,a semi-supervised discrete cosine transform(DCT) coefficient selection method was proposed to reduce dimensions and improve recognition accuracy.First,the DCT was performed on an image database,and useful features were selected by pre-masking based on frequency features.Second,with a few labeled samples,semi-supervised constrained clustering was used for clustering on training image sets.Then,higher discriminant coefficient values were obtained by class labels,and coefficient selection masking and projection of training images were carried out.Finally,the discrete cosine transform the test images was projected on the masking,the distance between test image projection and training image projection was computed,and a class of test images was estimated and classified based on the minimal distance classifier.Experimental results on ORL and Yale face databases show that the performance of the proposed method is better than traditional methods.Furthermore,the proposed method can be combined with principal component analysis(PCA) or linear discriminant analysis(LDA),and obtain more than 90% recognition rate.