Face Recognition Based On Generalized 2-Dimensional Complex Discriminant Analysis

A face recognition approach of generalized 2-dimensional(2D)complex discriminant analysis was proposed to tackle such problems that the projection matrix of 2D linear discriminant analysis for face image projection was non-orthogonal,the covariance information of different rows or columns which was conducive to discriminant analysis was very likely to get lost when only features in rows or columns were being extracted,and the dimensions where features existed were relatively high.The method includes two new methods proposed in this paper,namely the new generalized 2D linear discriminant analysis method and the new 2D combined complex discriminant analysis method.Firstly,generalized 2D linear discriminant analysis and its extension method were conducted on facial images,and the feature vectors are selected according to the feature value contribution rate to form the orthogonal projection matrix,then the projection of horizontal and vertical direction is completed.Secondly,the two types of feature matrices obtained after processing were added together in forms of real part and imaginary part of complex numbers,and the complex feature matrices were obtained by conducting new generalized 2D linear discriminant analysis on feature matrices having been fused.Then,the recognition performance of feature matrix components was measured based on feature values of complex feature matrices,the feature matrix components were re-ranked,and the most discriminative components were selected to form the final features characterizing human faces.At last,Cosine-norm maximum similarity classifier was used to classify and recognize features of human face images by comparing the similarity between the test samples and the training sample features.Experimental tests in multiple face databases show that the generalized 2D complex discriminant analysis method has higher accuracy and lower space occupancy for face recognition under complex conditions,among them,the new generalized 2D linear discriminant analysis improves the recognition accuracy of the method,and the new 2D combined complex discriminant analysis improves the mehtod's dimensionality reduction ability.This method can effectively overcome drawbacks such as poor feature extraction stability of 2D linear discriminant analysis,overlap of features in feature space,and high dimension of features,manifesting high robustness,great precision,and low space complexity.There are 27 figures,9 tables and 73 references in this paper.