Face recognition with single training sample per person based on generalized slide window and 2DLDA
Yongjun Liu · Journal of Computer Applications · 2007
For face recognition with single training sample per person,the conventional face recognition methods which work with many training samples do not function well.Especially,a number of methods based on Fisher linear discrimination criterion can not work because the within-class scatter matrix is a matrix with all elements being zero.To solve this problem,a new sample augment method,called generalized slide window,was proposed.In order to effectively maintain and strengthen the within-class and between-class information,the rule,big window,small step,was adopted to produce a set of window images for each training image.Then,two-dimensional Fisher linear discrimination analysis was performed on the window images obtained.The experimental results on ORL face database confirm that the proposed method is feasible and effective in face recognition with single training sample per person.