Face recognition of generalized parallel two-dimensional complex discriminate analysis
liuwanjun,bingxiaohuan,jiangwentao,zhangshengchong(Liaoning Technical University)
Objective A face recognition approach of generalized parallel two-dimensional (2D) complex discriminant analysis was proposed to tackle such problems that 2D linear discriminant analysis demonstrated poor stability when extracting facial feature vectors, 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. Method Firstly, generalized parallel 2D linear discriminant analysis was 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 generalized 2D complex 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; and at last, 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. Result Yale, ORL, FERET, CMU-PIE and LFW face databases were experimented, from which the optimal recognition rates obtained by using this method were respectively 100%, 100%, 98.98%, 99.76%, and 98.67%, with the feature dimensions ranging from 85 to 90, which indicated that this method delivered relatively high face recognition precision and low space occupancy in complex conditions. Conclusion This method could effectively overcome drawbacks such as poor feature extraction stability of 2D linear discriminant analysis, overlap of features in feature space, excessive storage coefficients, and high dimension of features, manifesting high robustness, great precision, and low space complexity.