Face recognition using the most representative sift images

被引：0
|
作者：
Dagher, Issam [1 ]
Sallak, Nour El [1 ]
Hazim, Hani [1 ]
机构：
[1] University of Balamand, Department of Computer Engineering, Lebanon
来源：
International Journal of Signal Processing, Image Processing and Pattern Recognition | 2014年 / 7卷 / 01期
关键词：
Clustering algorithms;
D O I：
10.14257/ijsip.2014.7.1.21
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, face recognition using the most representative SIFT images is presented. It is based on obtaining the SIFT (SCALE INVARIANT FEATURE TRANSFORM) features in different regions of each training image. Those regions were obtained using the K-means clustering algorithm applied on the key-points obtained from the SIFT algorithm. Based on these features, an algorithm which will get the most representative images of each face is presented. In the test phase, an unknown face image is recognized according to those representative images. In order to show its effectiveness this algorithm is compared to other SIFT algorithms and to the LDP algorithm for different databases. © 2014 SERSC.
引用
收藏
页码：225 / 236
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