Orthogonal Discriminant Diversity and Similarity Preserving Projection for Face Recognition

School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, 232001, China

^a Corresponding author: yelin@aust.edu.cn

Abstract

Feature extraction is a crucial step for face recognition. In this paper, based on supervised local structure and diversity projection (SLSDP), a new feature extraction method called orthogonal discriminant diversity and similarity preserving projection (ODDSPP) is proposed for face recognition. ODDSPP defines two parameterless weighted matrices by taking into account the class label information and local structure. Thus ODDSPP could utilize both the diversity and similarity information of the data simultaneously for dimensionality reduction. Moreover, the proposed algorithm is able to extract the orthogonal discriminant vectors in the feature space and does not suffer from the small sample size problem, which is desirable for many pattern analysis applications. Experimental results on the ORL and AR databases show the effectiveness of the proposed method.