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General Information
    • ISSN: 2010-3700
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Engineering & Technology Digital Library, Google Scholar, Crossref, ProQuest, Electronic Journals Library, DOAJ and EI (INSPEC, IET).
    • E-mail: ijmlc@ejournal.net
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2013 Vol.3(4): 389-392 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.345

Periocular Biometric Recognition Using Supervised Fuzzy Clustering

Vivek Srivastava, Bipin K. Tripathi, and Vinay K. Pathak
Abstract—Developing a potential biometrics has been a key focus of research in recent years. Periocular biometrics is a new trait to deal with non-ideal scenarios in face and iris biometrics. It can be used as an alternative to iris recognition, if the iris images are captured at a distance. In forensic applications, this trait can be used individually as well as with other traits (face and iris) for effective and accurate identification. In recent researches, the periocular biometrics is significantly impacting the iris and face based recognition. In this paper, we investigated the efficacy of supervised fuzzy clustering for strict periocular region which does not involve the eyebrows. The fixed initialization is considered in proposed supervised fuzzy clustering instead of random initialization. Then fuzzy clustering motivated with partition index maximization is used to optimize the objective function, hence yield clusters with representative prototype. The fuzzy clustering is further generalized with Minkowski distance matrices to yield variable cluster shape. Recognition is done based on the minimum distance measure between the test patterns and the centroid of the clusters. We use eight hundred periocular region images extracted from AR face dataset of 40 subjects. Performance of the proposed technique has been evaluated in terms of rank-one and rank- two recognition accuracy. Experimental analysis demonstrates the efficacy of presented technique over other variants of fuzzy clustering techniques.

Index Terms—Periocular biometrics, fuzzy clustering, supervised initialization, principal component analysis.

Vivek Srivastava is with the National Institute of Standards and Technology, Boulder, CO 80305 USA (e-mail: author@ boulder.nist.gov). Bipin K. Tripathi was with Rice University, Houston, TX 77005 USA. He is now with the Department of Physics, Colorado State University, Fort Collins, CO 80523 USA (e-mail: author@lamar.colostate.edu). Vinay K. Pathak is with the Electrical Engineering Department, University of Colorado, Boulder, CO 80309 USA, on leave from the National Research Institute for Metals, Tsukuba, Japan (e-mail: author@nrim.go.jp).

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Cite:Vivek Srivastava, Bipin K. Tripathi, and Vinay K. Pathak, "Periocular Biometric Recognition Using Supervised Fuzzy Clustering," International Journal of Machine Learning and Computing vol.3, no. 4, pp. 389-392, 2013.

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