Face Recognition Using Clustering Based Optimal Linear Discriminant Analysis
Jian Chen · Jisuanji kexue yu tansuo · 2009
Recently,current researches indicated that,the angle between the eigenvector corresponding to the largest eigenvalue of the inter-class covariance and the eigenvector corresponding to the largest eigenvalue of the intra-class covariance is more crucial to the performance of traditional linear discriminant methods,furthermore,if the two eigenvectors are parallel;the final results may be disputable. However,upon careful scrutiny on his assertion,conclude that the angle between the two eigenvectors is less decisive to the performance,more over;the main drawback of traditional linear methods is the inter-class covariance cannot precisely reflect the discriminant infomation. Simply maximizing the inter-class covariance in the principle component space may induce the losing of adjacent class-pair's contribution. Therefore,propose the optimal linear discriminant analysis(OLDA) method, which distributes equivalent authority for each class-pair by employingdiscriminative power. Besides,employ the gradient scheme to derive the feature vectors,and a constraint condition is introduced to evaluate the convergence speed. Thirdly,to address the multimodal problem,the pre-clustering mechanism is adopted to ameliorate the non-linear structure. Apply this method on a practical face database and a virtual database,the experimental results show the promise of this method.