Face recognition using difference vector plus KPCA

doi:10.1016/j.dsp.2011.08.004

Digital Signal Processing

Volume 22, Issue 1, January 2012, Pages 140-146

https://doi.org/10.1016/j.dsp.2011.08.004 Get rights and content

Abstract

In this paper, a novel approach for face recognition based on the difference vector plus kernel PCA is proposed. Difference vector is the difference between the original image and the common vector which is obtained by the images processed by the Gram–Schmidt orthogonalization and represents the common invariant properties of the class. The optimal feature vectors are obtained by KPCA procedure for the difference vectors. Recognition result is derived from finding the minimum distance between the test difference feature vectors and the training difference feature vectors. To test and evaluate the proposed approach performance, a series of experiments are performed on four face databases: ORL, Yale, FERET and AR face databases and the experimental results show that the proposed method is encouraging.

Section snippets

Ying Wen received her B.Sc. degree in industry automation from Technology of Hefei University in 1997 and the M.Sc. and Ph.D. degrees in imaging processing and pattern recognition from Shanghai University and Shanghai Jiao Tong University, China, in 2002 and 2009 respectively. She is an associate professor at East China Normal University and currently does research as a Postdoctoral Research Fellow at Columbia University in the city of New York. Her research interests include documentary

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Lianghua He received his B.Sc. degree in engineering survey from Technology of Wuhan Technology of Surveying and Mapping in 1999 and his M.Sc. and Ph.D. degrees in GPS Navigation and signal and information processing from Wuhan University and Southeast University in 2002 and 2005 respectively. He currently does research as an associate professor at Tongji University. His research interests include image processing, pattern recognition, machine learning and medical image processing.

Pengfei Shi received the Bachelorʼs and Masterʼs degree in electrical engineering from Shanghai Jiao Tong University (SJTU), Shanghai, China in 1962 and 1965, respectively. In 1980, he joined the Institute of Image Processing and Pattern Recognition (IPPR), SJTU. During the past 30 years, he worked in the area of image analysis, pattern recognition and visualization. He has published more than 100 papers. He is at present the Director of the Institute of IPPR at SJTU and Professor of pattern recognition and intelligent system in the Faculty of Electronic and Information Engineering. He is a senior member of the IEEE.

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Face recognition using difference vector plus KPCA

Abstract

Section snippets

Pattern Recognition

Computer Vision and Image Understanding

Neural Networks

Pattern Recognition

Pattern Recognition

Neurocomputing

Noniterative 3D face reconstruction based on photometric stereo

International Journal of Pattern Recognition and Artificial Intelligence Biometrics

Extracting faces and facial features from color images

International Journal of Pattern Recognition and Artificial Intelligence Biometrics

An improved locally linear embedding for sparse data sets

International Journal of Pattern Recognition and Artificial Intelligence

Algorithms for accelerated convergence of adaptive PCA

IEEE Transactions on Neural Networks