Most information feature extraction (MIFE) approach for face recognition

被引：0

作者：

Zhao, JL ^{[1
]}

Ren, HB ^{[1
]}

Wang, HT ^{[1
]}

Kee, S ^{[1
]}

机构：

[1] Chinese Acad Sci, Samsung AIT, Beijing Lab,Inst Automat, CASIA SAIT HCI Joint Lab, Beijing 100080, Peoples R China

来源：

BIOMETRIC TECHNOLOGY FOR HUMAN IDENTIFICATION II | 2005年 / 5779卷

关键词：

face recognition; feature extraction; face recognition with different illumination; pattern recognition;

D O I：

10.1117/12.601880

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a MIFE (Most Information Feature Extraction) approach, which extract as abundant as possible information for the face classification task. In the MIFE approach, a facial image is separated into sub-regions and each sub-region makes individual's contribution for performing face recognition. Specifically, each sub-region is subjected to a sub-region based adaptive gamma (SadaGamma) correction or sub-region based histogram equalization (SHE) in order to account for different illuminations and expressions. Experiment results show that the proposed SadaGamma/SHE correction approach provides an efficient delighting solution for face recognition. MIFE and SadaGamma/SHE correction together achieves lower error ratio in face recognition under different illumination and expression.

引用

页码：381 / 389

页数：9

共 11 条

[1]

[Anonymous], FACIAL RECOGNITION V

[2] Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection [J].

Belhumeur, PN ;

Hespanha, JP ;

Kriegman, DJ .

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1997, 19 (07) :711-720

[3]

Chen HF, 2000, PROC CVPR IEEE, P254, DOI 10.1109/CVPR.2000.855827

[4]

Georghiades A. S., 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580), P277, DOI 10.1109/AFGR.2000.840647

[5] Learning a locality preserving subspace for visual recognition [J].