vioft2nntf2t|tblJournal|Abstract_paper|0xf4ffcebb03000000f10e000001000200 The feature extraction plays a very important role in iris recognition. Recent researches on multiscale analysis provide good opportunity to extract more accurate information for iris recognition. In this work, a new directional iris texture features based on 2-D Fast Discrete Curvelet Transform (FDCT) is proposed. The proposed approach divides the normalized iris image into six sub-images and the curvelet transform is applied independently on each sub-image. The anisotropic feature vector for each sub-image is derived using the directional energies of the curvelet coefficients. These six feature vectors are combined to create the resultant feature vector. During recognition, the nearest neighbor classifier based on Euclidean distance has been used for authentication. The effectiveness of the proposed approach has been tested on two different databases namely UBIRIS and MMU1. Experimental results show the superiority of the proposed approach.
Amol D. Rahulkar1, Dattatraya V. Jadhav2, Raghunath S. Holambe3
AISSMS Institute of Information Technology, India1, Bhivarabai Sawant College of Engineering and Research, India2, SGGS Institute of Engineering and Technology, India3
Iris Recognition, Biometrics, Curvelet Transform, FDCT, Feature Extraction
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 1 , Issue: 2 , Pages: 69 - 75 )
Date of Publication :
November 2010
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