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: firstname.lastname@example.org). 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: email@example.com).
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.