Abstract

OLIVARES-MERCADO, Jesús et al. Modifications to the Eigenphases Method for Face Recognition Based on SVM. Ing. invest. y tecnol. [online]. 2016, vol.17, n.1, pp.119-129. ISSN 2594-0732.

This paper presents two modifications to the eigenphases method to increase its accuracy. In the first modification, called Local Spatial Domain Eigenphases (LSDE), the face image is first segmented into blocks of N × N pixels, whose magnitudes are normalized. These blocks are then concatenated before the phase spectrum estimation, and finally Principal Component Analysis (PCA) is used for dimensionality reduction. In the second modification, called Local Frequency Domain Eigenphases (LFDE), first the face image is segmented into blocks of pixels, whose pixels are normalized. The phase spectrum of each block is estimated independently. Next, the phase spectra of all the blocks are concatenated and then are applied to the PCA stage for dimensionality reduction. The proposed approaches are evaluated using open-set and closed-set face identification, as well as identity verification, using the "AR Face Database." The evaluation results show that the proposed modifications, using the Support Vector Machine as the classifier, perform fairly well under different illumination and partial occlusion conditions.

Keywords : eigenphases; gabor transform; discrete wavelet transform; principal components analysis; support vector machine.

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