Iris Recognition with Multi-Scale Edge-Type Matching

2006 ◽  
Author(s):  
Chia-Te Chou ◽  
Sheng-Wen Shih ◽  
Wen-Shiung Chen ◽  
V.W. Cheng
Keyword(s):  
Iris Recognition ◽  
Edge Type ◽  
Multi Scale

Iris Recognition Method Based on Feature Discrimination and Category Correlation

2014 ◽  
Vol 602-605 ◽  
pp. 1610-1613
Author(s):  
Ming Hai Yao ◽  
Na Wang ◽  
Jin Song Li
Keyword(s):  
Iris Recognition ◽  
Predictive Accuracy ◽  
Gabor Filter ◽  
Recognition Algorithm ◽  
Recognition Method ◽  
Texture Characteristic ◽  
Multi Scale ◽  
Laplacian Score ◽  
Circular Gabor Filter ◽  
Academy Of Sciences

With the increasing number of internet user, the authentication technology is more and more important. Iris recognition as an important method for identification, which has been attention by researchers. In order to improve the predictive accuracy of iris recognition algorithm, the iris recognition method is proposed based feature discrimination and category correlation. The feature discrimination and category correlation are calculated by laplacian score and mutual information. The formula about feature discrimination and category correlation are built. Aiming at texture characteristic of iris image, the multi-scale circular Gabor filter is used to feature extraction. The computational efficiency of algorithm is improved. In order to verify the validity of the algorithm, the CASIA iris database of Chinese Academy of Sciences is used to do the experiment. The experimental results show that our method has high predictive accuracy.

scholarly journals FMnet: Iris Segmentation and Recognition by Using Fully and Multi-Scale CNN for Biometric Security

2019 ◽  
Vol 9 (10) ◽  
pp. 2042 ◽  
Author(s):  
Rachida Tobji ◽  
Wu Di ◽  
Naeem Ayoub
Keyword(s):  
Neural Networks ◽  
Iris Recognition ◽  
State Of The Art ◽  
Features Extraction ◽  
Iris Segmentation ◽  
Recognition Method ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Multi Scale ◽  
Biometric Security

In Deep Learning, recent works show that neural networks have a high potential in the field of biometric security. The advantage of using this type of architecture, in addition to being robust, is that the network learns the characteristic vectors by creating intelligent filters in an automatic way, grace to the layers of convolution. In this paper, we propose an algorithm “FMnet” for iris recognition by using Fully Convolutional Network (FCN) and Multi-scale Convolutional Neural Network (MCNN). By taking into considerations the property of Convolutional Neural Networks to learn and work at different resolutions, our proposed iris recognition method overcomes the existing issues in the classical methods which only use handcrafted features extraction, by performing features extraction and classification together. Our proposed algorithm shows better classification results as compared to the other state-of-the-art iris recognition approaches.

scholarly journals Multi Scale ICA Based IRIS Recognition Using BSIF and HOG

2017 ◽  
Vol 8 (6) ◽  
pp. 11-31 ◽  
Author(s):  
Ganapathi V Sagar ◽  
Abidali Munna NC ◽  
Suresh Babu K ◽  
Raja K B ◽  
Venugopal K R
Keyword(s):  
Iris Recognition ◽  
Multi Scale

scholarly journals The Optimal Matching Algorithm for Multi-Scale Iris Recognition

2012 ◽  
Vol 16 ◽  
pp. 876-882 ◽  
Author(s):  
XiaoZhou Chen ◽  
ChangYin Wu ◽  
LiangLin Xiong ◽  
Fan Yang
Keyword(s):  
Iris Recognition ◽  
Optimal Matching ◽  
Matching Algorithm ◽  
Multi Scale

Iris recognition by fusing different representations of multi-scale Taylor expansion

2011 ◽  
Vol 115 (6) ◽  
pp. 804-816 ◽  
Author(s):  
Algirdas Bastys ◽  
Justas Kranauskas ◽  
Volker Krüger
Keyword(s):  
Taylor Expansion ◽  
Iris Recognition ◽  
Multi Scale
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