An Iris Recognition System by Laws Texture Energy Measure Based k-NN Classifier

被引：0

作者：

Acar, Emrullah ^{[1
]}

Ozerdem, Mehmet Sirac ^{[2
]}

机构：

[1] BATMAN Univ, Elekt & Elekt Muhendisligi Bolumu, Batman, Turkey

[2] DICLE Univ, Dept Elect & Electron Engn, Diyarbakir, Turkey

来源：

2013 21ST SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 2013年

关键词：

Iris Recognition; Image Processing; Classification; k-NN Classifier; Laws TEM;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Biometric recognition technology is correlated generally with very expensive top secure applications. Iris recognition system is one of the effective biometric recognition systems. The main purpose of this study is to recognize the human from different eye images according to their iris texture characteristics. The digital crop images are derived from CASIA iris image database. The texture feature vectors are extracted from the local iris regions by using Laws Texture Energy Measure (TEM) which is a new method for image texture feature extraction. The obtained feature vectors are separated by k-Nearest Neighbor (k-NN) classifier as taking the neighbor number (k) parameter in different values and the performance results of each system are compared according to disparate k values. Finally, the best average performance is observed as 80.74 % in k=1 and 2 neighbors structure of k-NN classifier.

引用

页数：4

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