SPECTRAL RANGE SELECTION FOR FACE RECOGNITION UNDER VARIOUS ILLUMINATIONS

被引：8

作者：

Chang, H. ^{[1
]}

Yao, Y. ^{[1
]}

Koschan, A. ^{[1
]}

Abidi, B. ^{[1
]}

Abidi, M. ^{[1
]}

机构：

[1] Univ Tennessee, IRIS Lab, Knoxville, TN 37996 USA

来源：

2008 15TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-5 | 2008年

关键词：

face recognition; multispectral imaging; Jeffrey divergence; kernel density estimation; IMAGES;

D O I：

10.1109/ICIP.2008.4712365

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Multipectral imaging and fusion of multispectral images in visible spectrum have been employed and proved to enhance the recognition performance over conventional broad-band images, especially when the illumination changes. In this paper, we further our study toward the recognition performance of a smaller number of spectral band images. Given the illumination, we propose an automated method that specifies the optimal spectral ranges to achieve an improved recognition performance compared with conventional broad-band images. Our discovery can be practically used for a new customized sensor design associated with given illuminations for an improved face recognition performance over conventional broad-band images.

引用

页码：2756 / 2759

页数：4

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