JACIII Vol.23 No.3 pp. 465-473
doi: 10.20965/jaciii.2019.p0465


Improvement of Face Recognition with Gabor, PCA, and SVM Under Complex Illumination Conditions

Liyun Zhuang*,** and Yepeng Guan*,***,†

*School of Communications and Information Engineering, Shanghai University
99 Shangda Road, Baoshan District, Shanghai 200444, China

**Faculty of Electronic and Information Engineering, Huaiyin Institute of Technology
No.1 Meicheng East Road, Huaian, Jiangsu 223003, China

***Key Laboratory of Advanced Displays and System Application, Ministry of Education
Shanghai, China

Corresponding author

July 3, 2018
November 22, 2018
May 20, 2019
complex illumination, face recognition, full illumination variation, principal component analysis, support vector machine

Complex illumination condition is one of the most critical challenging problems for practical face recognition. However, numerous studies have had no effective solutions reported for full illumination variation of face images in the facial recognition research field. In order to effectively solve full illumination variation problem, we propose a novel approach for illumination normalization for facial images based on the enhanced contrast method of histogram equalization (HE) and fusion of illumination estimations (FOIE). Then, feature extraction is applied with consideration of both Gabor wavelet and principal component analysis methods to process illumination normalization. Next, a support vector machine classifier (SVM) is used for face classification. Experimental results show that superior performance can be obtained in the developed approach by comparisons with some state-of-the-arts.

Cite this article as:
L. Zhuang and Y. Guan, “Improvement of Face Recognition with Gabor, PCA, and SVM Under Complex Illumination Conditions,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.3, pp. 465-473, 2019.
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Last updated on Sep. 19, 2019