Modified algorithm of two-dimension LDA for face recognition
Yanjiang Wang, Juan Fang, Peng Suo · 2008
A modified two-dimension linear discriminant analysis (2DLDA) algorithm is proposed. After lighting compensation, the input face images are processed first by singular value decomposition (SVD) perturbation and wavelet transform, and then the 2DLDA is used to extract the features, whose dimension is further reduced by PCA algorithm. At last, the support vector machines (SVM) classifier is used for classification and recognition. Experimental results on ORL database show that the proposed method has better recognition performance than other PCA- and LDA-based algorithms.