Iris Recognition Using 3D Co-occurrence Matrix

GLCM-Based Multiclass Iris Recognition Using FKNN and KNN

International Journal of Image and Graphics ◽

10.1142/s0219467814500107 ◽

2014 ◽

Vol 14 (03) ◽

pp. 1450010 ◽

Cited By ~ 2

Author(s):

S. B. Kulkarni ◽

Raghavendrarao B. Kulkarni ◽

U. P. Kulkarni ◽

Ravindra S. Hegadi

Keyword(s):

Iris Recognition ◽

Nearest Neighbor ◽

Region Of Interest ◽

K Nearest Neighbor ◽

Single Class ◽

Iris Image ◽

Authentication Mechanism ◽

Knn Classification ◽

Occurrence Matrix ◽

Matching Techniques

Iris recognition is one of the important authentication mechanism used extensively in biometric applications. The majority of the applications use single class iris recognition with normalized iris image. The proposed technique uses multi class iris recognition with region of interest (ROI) iris image on supervised learning. In this paper, the term ROI is referred as Un-normalized iris. The iris features are extracted using gray level co-occurrence matrix (GLCM) and a multiclass training vector is created. Further, iris image is classified based on fuzzy K-nearest neighbor (FKNN) and KNN classification. Test samples features are matched with the stored repository by various matching techniques such as max fuzzy vote, Euclidean distance, cosine and cityblock. The experiment is carried on standard database CASIA-IrisV3-Interval and result shows that multiclass approach with ROI segmented iris has better recognition accuracy using FKNN and KNN.

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Second-Order Statistical Methods GLCM for Authentication Systems

Iraqi Journal for Electrical And Electronic Engineering ◽

10.37917/ijeee.17.1.10 ◽

2021 ◽

Vol 17 (1) ◽

pp. 1-6

Author(s):

Mohammed Taha ◽

Hanaa Ahmed

Keyword(s):

Iris Recognition ◽

Near Infrared ◽

Color Image ◽

Visible Spectrum ◽

Second Order ◽

Infrared Light ◽

Identification System ◽

Extraction Technology ◽

Occurrence Matrix ◽

Iris Identification

For many uses, biometric systems have gained considerable attention. Iris identification was One of the most powerful sophisticated biometrical techniques for effective and confident authentication. The current iris identification system offers accurate and reliable results based on near-infrared light (NIR) images when images are taken in a restricted area with fixed-distance user cooperation. However, for the color eye images obtained under visible wavelength (VW) without collaboration among the users, the efficiency of iris recognition degrades because of noise such as eye blurring images, eye lashing, occlusion, and reflection. This work aims to use the Gray-Level Co- occurrence Matrix (GLCM) to retrieve the iris's characteristics in both NIR iris images and visible spectrum. GLCM is second-order Statistical-Based Methods for Texture Analysis. The GLCM-based extraction technology was applied after the preprocessing method to extract the pure iris region's characteristics. The Energy, Entropy, Correlation, homogeneity, and Contrast collection of second-order statistical features are determined from the generated co-occurrence matrix, Stored as a vector for numerical features. This approach is used and evaluated on the CASIA v1and ITTD v1 databases as NIR iris image and UBIRIS v1 as a color image. The results showed a high accuracy rate (99.2 %) on CASIA v1, (99.4) on ITTD v1, and (87%) on UBIRIS v1 evaluated by comparing to the other methods

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Iris Recognition using Gray Level Co-occurrence Matrix and Hausdorff Dimension

International Journal of Computer Applications ◽

10.5120/ijca2016907953 ◽

2016 ◽

Vol 133 (8) ◽

pp. 29-34 ◽

Cited By ~ 2

Author(s):

Amol M. ◽

Dilip S. ◽

Pravin S.

Keyword(s):

Hausdorff Dimension ◽

Iris Recognition ◽

Gray Level ◽

Occurrence Matrix

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Keeping an Eye on School Security: The Iris Recognition Project in New Jersey Schools

PsycEXTRA Dataset ◽

10.1037/e538032006-003 ◽

2006 ◽

Author(s):

Jeffrey P. Cohn

Keyword(s):

New Jersey ◽

Iris Recognition ◽

School Security

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IRIS AND FINGER VEIN MULTI MODEL RECOGNITION SYSTEM BASED ON SIFT FEATURES

Journal of Advanced Sciences and Engineering Technologies ◽

10.32441/jaset.v1i2.119 ◽

2018 ◽

Vol 1 (2) ◽

pp. 34-44

Author(s):

Faris E Mohammed ◽

Dr. Eman M ALdaidamony ◽

Prof. A. M Raid

Keyword(s):

Iris Recognition ◽

Recognition Performance ◽

Recognition System ◽

Individual Identification ◽

Work Place ◽

Identification Process ◽

Finger Vein ◽

Noise Point ◽

Vein Recognition ◽

A New Technique

Individual identification process is a very significant process that resides a large portion of day by day usages. Identification process is appropriate in work place, private zones, banks …etc. Individuals are rich subject having many characteristics that can be used for recognition purpose such as finger vein, iris, face …etc. Finger vein and iris key-points are considered as one of the most talented biometric authentication techniques for its security and convenience. SIFT is new and talented technique for pattern recognition. However, some shortages exist in many related techniques, such as difficulty of feature loss, feature key extraction, and noise point introduction. In this manuscript a new technique named SIFT-based iris and SIFT-based finger vein identification with normalization and enhancement is proposed for achieving better performance. In evaluation with other SIFT-based iris or SIFT-based finger vein recognition algorithms, the suggested technique can overcome the difficulties of tremendous key-point extraction and exclude the noise points without feature loss. Experimental results demonstrate that the normalization and improvement steps are critical for SIFT-based recognition for iris and finger vein , and the proposed technique can accomplish satisfactory recognition performance. Keywords: SIFT, Iris Recognition, Finger Vein identification and Biometric Systems. © 2018 JASET, International Scholars and Researchers Association

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