An Iris Recognition Algorithm Using Local Extreme Points

2004 ◽  
pp. 442-449 ◽  
Author(s):  
Jiali Cui ◽  
Yunhong Wang ◽  
Tieniu Tan ◽  
Li Ma ◽  
Zhenan Sun
Keyword(s):  
Iris Recognition ◽  
Extreme Points ◽  
Recognition Algorithm ◽  
Local Extreme

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.

Iris Localisation Using Fuzzy Centre Detection (FCD) Scheme and Active Contour Snake

2014 ◽  
Vol 69 (6) ◽  
Author(s):  
Masrullizam Mat Ibrahim ◽  
John S. Soraghan ◽  
Nurulfajar Abd Manap
Keyword(s):  
Active Contour ◽  
Iris Recognition ◽  
Image Database ◽  
Target Object ◽  
Recognition Algorithm ◽  
Experimental Result ◽  
Georgia Tech ◽  
Fuzzy Membership Functions ◽  
Main Target ◽  
Snake Algorithm

Iris localisation is a crucial operation in iris recognition algorithm and also in applications, where irises are the main target object. This paper presents a new method to localise iris by using Fuzzy Centre Detection (FCD) scheme and active contour Snake. FCD scheme which consists of four fuzzy membership functions is purposely designed to find a centre of the iris. By using the centre of iris as the reference point, an active contour Snake algorithm is employed to localise the inner and outer of iris boundary. This proposed method is tested and validated with two categories of image database; iris databases and face database.  For iris database, UBIRIS.v1, UBIRIS.v2, CASIA.v1, CASIA.v2, MMU1 and MMU2 are used. Whilst for face databases, MUCT, AT&T, Georgia Tech and ZJUblink databases are chosen to evaluate the capability of proposed method to deal with the small size of the iris in the image database. Based on the experimental result, the proposed method shows promising results for both types of databases, including comparison with the some existing iris localisation algorithm.  

Local extreme points and a Young-type inequality

2018 ◽  
Vol 12 (26) ◽  
pp. 1265-1275
Author(s):  
Loredana Ciurdariu
Keyword(s):  
Type Inequality ◽  
Extreme Points ◽  
Local Extreme

scholarly journals Performances of proposed normalization algorithm for iris recognition

2020 ◽  
Vol 6 (2) ◽  
pp. 161 ◽  
Author(s):  
Yessi Jusman ◽  
Siew Cheok Ng ◽  
Khairunnisa Hasikin
Keyword(s):  
Iris Recognition ◽  
Correlation Energy ◽  
Texture Feature ◽  
Region Of Interest ◽  
Classification Performance ◽  
Recognition Algorithm ◽  
Small Constant ◽  
Hough Transforms ◽  
Effective System ◽  
High Recognition Accuracy

Iris recognition has very high recognition accuracy in comparison with many other biometric features. The iris pattern is not the same even right and left eye of the same person. It is different and unique. This paper proposes an algorithm to recognize people based on iris images. The algorithm consists of three stages. In the first stage, the segmentation process is using circular Hough transforms to find the region of interest (ROI) of given eye images. After that, a proposed normalization algorithm is to generate the polar images than to enhance the polar images using a modified Daugman’s Rubber sheet model. The last step of the proposed algorithm is to divide the enhance the polar image to be 16 divisions of the iris region. The normalized image is 16 small constant dimensions. The Gray-Level Co-occurrence Matrices (GLCM) technique calculates and extracts the normalized image’s texture feature. Here, the features extracted are contrast, correlation, energy, and homogeneity of the iris. In the last stage, a classification technique, discriminant analysis (DA), is employed for analysis of the proposed normalization algorithm. We have compared the proposed normalization algorithm to the other nine normalization algorithms. The DA technique produces an excellent classification performance with 100% accuracy. We also compare our results with previous results and find out that the proposed iris recognition algorithm is an effective system to detect and recognize person digitally, thus it can be used for security in the building, airports, and other automation in many applications.

The Design of Library Management System Based on Internet of Things and Hand Lines Recognition System

2014 ◽  
Vol 672-674 ◽  
pp. 1985-1990 ◽  
Author(s):  
Wang Fang
Keyword(s):  
Management System ◽  
Iris Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Library Management ◽  
Material Resources ◽  
Library Management System ◽  
Biometric Systems

For an ordinary individual biometric systems and technology, such as fingerprint recognition, palm recognition, face recognition or iris recognition, or late detection from a single object has crippled so that they have the characteristics of unity and limitations, this paper combining fingerprint and hand palm pattern recognition technology, taking into account the complexity of the image pattern and diversity, we propose a dual recognition algorithm, which greatly makes up for lack of a single fingerprint or palm print recognition method. The technology used in library management system than traditional card-borrowed books have higher efficiency and save manpower and material resources. After the experimental statistics, and achieved the desired results, not only improve the recognition efficiency, but also to ensure the accuracy of the recognition performance.

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