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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(1): 1-6 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.80

Illumination Invariant Face Recognition Using the Statistical Features of BDIP and Wavelet Transform

Amany Farag and Randa Atta

Abstract—In this paper, an efficient feature extraction method based on local statistics features of block difference of inverse probabilities (BDIP) and the wavelet transform is proposed for face recognition. In the proposed method, the BDIPs are first computed in a face in order to overcome the variation of illumination and facial expressions. The obtained BDIP image is then decomposed into wavelet subbands. In order to reduce the dimensionality of the feature vector, each BDIP subband is partitioned into a set of blocks. The means and variances are then calculated from all the blocks in each subband and are fused into a feature vector. Experimental results on ORL and FERET databases show that the proposed method achieves higher recognition accuracies than the wavelet-based methods with higher dimensionality reduction of the feature vector. It also outperforms the other well known methods such as PCA and the DCT with the zigzag scanning.

Index Terms—Face recognition (FR), discrete wavelet transforms (DWT), wavelet packet decomposition (WPD), block difference of inverse probabilities (BDIP), support vector machine (SVM).

A. Farag is with the Computer Science Department, Port Said University, Port Said, Egypt (e-mail: amanyfarag@hotmail.com).
R. Atta is with the Electrical Engineering Department, Faculty of Engineering, Port Said University, Port Said, Port Fouad 42523, Egypt (e-mail: r.atta@eng.psu.edu.eg).

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Cite: Amany Farag and Randa Atta, "Illumination Invariant Face Recognition Using the Statistical Features of BDIP and Wavelet Transform," International Journal of Machine Learning and Computing vol. 2, no. 1, pp. 1-6, 2012.

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