Super-Resolution for Iris Feature Extraction

被引：0
|
作者：
Deshpande, Anand [1 ]
Patavardhan, Prashant P. [1 ]
Rao, D. H. [2 ]
机构：
[1] Gogte Inst Technol, Dept Elect & Commun Engn, Belgaum, India
[2] Visvesvaraya Technol Univ, Dept PG Studies, Belgaum, India
来源：
2014 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMPUTING RESEARCH (IEEE ICCIC) | 2014年
关键词：
Super-resolution; Iris; Papoulis-Gerchberg; Projection onto Convex Sets; GLCM;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Super-resolution technique can be used to fix the low resolution problem for recognizing the iris at a distance. Two frequency domain super-resolution algorithms, Papoulis-Gerchberg (PG) and Projection onto Convex Sets, are implemented to increase the resolution of iris images. The performance analysis of these algorithms is carried out by extracting Gray Level Co-occurrenceMatrix (GLCM) features of super-resoluted iris images. The super-resoluted iris region is normalized, extracted GLCM features and compared with the GLCM features of normalized original iris region. It has been observed that the GLCM features reconstructed images using above algorithm closely matches with that of original iris image. The error between the GLCM features of original normalized and normalized super-resoluted image using Papoulis-Gerchberg is less compared to that of Projection onto Convex Sets.
引用
收藏
页码：1123 / 1126
页数：4
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