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

被引:2
|
作者
Zhuang, Liyun [1 ,2 ]
Guan, Yepeng [1 ,3 ]
机构
[1] Shanghai Univ, Sch Commun & Informat Engn, 99 Shangda Rd, Shanghai 200444, Peoples R China
[2] Huaiyin Inst Technol, Fac Elect & Informat Engn, 1 Meicheng East Rd, Huaian 223003, Jiangsu, Peoples R China
[3] Minist Educ, Key Lab Adv Displays & Syst Applicat, Shanghai, Peoples R China
关键词
complex illumination; face recognition; full illumination variation; principal component analysis; support vector machine; NORMALIZATION; CLASSIFICATION; COMPENSATION; ENHANCEMENT; TRANSFORM; INVARIANT;
D O I
10.20965/jaciii.2019.p0465
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:465 / 473
页数:9
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