New Method using Feature Level Image Fusion and Entropy Component Analysis for Multimodal Human Face Recognition

被引：5
|
作者：
Wu, Tao [1 ]
Wu, Xiao-Jun [1 ]
Liu, Xing [1 ]
Luo, Xiao-Qing [1 ]
机构：
[1] Jiangnan Univ, Sch IoT Engn, Wuxi 214122, Peoples R China
来源：
2012 INTERNATIONAL WORKSHOP ON INFORMATION AND ELECTRONICS ENGINEERING | 2012年 / 29卷
关键词：
Face recognition; image fusion; PCNN; ECA; 2DECA; entropy contribution; region segmentation; COUPLED NEURAL-NETWORK;
D O I：
10.1016/j.proeng.2012.01.607
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
Visual and infrared cameras have complementary properties and using them together may increase the performance of human face recognition. This study presents a new efficient method for face recognition which fusing the complementary information from both domains. The fused image is obtained by a new image fusion method based on region segmentation and PCNN for the first step. In the second step, features of the fused images are extracted by ECA and 2DECA according to the entropy contribution. The method has been tested on OTCBVS database. Comparison of the experimental results shows that the proposed approach performs significantly well in face recognition. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Harbin University of Science and Technology
引用
收藏
页码：3991 / 3995
页数：5
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