Multiobjective Optimal Uncorrelated Image Discriminant Analysis in Face Recognition
人脸识别中多目标最优不相关图像鉴别分析研究

This paper addressed the image discriminant analysis problem.By constructing the scattering matrices of the image matrices,Foley-Sammon discriminant analysis(FSLDA)are transformed into a bi-objective optimization problem with uncorrelated constraint for improving the speed of feature extraction and the recognition rate.The efficient projection vector is defined and the efficient projection vector can be obtained from deciding the eigenvector corresponding to the eigenvalue of maximum of a generalized eigen-equation.Compared with the other image projection analysis methods,the proposed method has the following properties:(1)the scattering matrices are directly based on image matrices;(2)the efficient projection vectors are statistically uncorrelated;(3)the within scattering matrix is not necessarily invertible and some matrix inversions are not performed.Finally,the proposed method is tested on ORL and NUST603 face databases.The experimental results indicate that the recognition performance of the proposed method is prior to the other methods,and its speed for feature extraction is faster than the above methods.