MATEC Web of Conferences
Volume 61, 2016The International Seminar on Applied Physics, Optoelectronics and Photonics (APOP 2016)
|Number of page(s)||4|
|Section||Chapter 2 Electronic Technology and Electrical Engineering|
|Published online||28 June 2016|
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: firstname.lastname@example.org
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.
© Owned by the authors, published by EDP Sciences, 2016
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