Title: An improved sclera recognition using kernel entropy component analysis method

Authors: B.S. Harish; M.S. Maheshan; C.K. Roopa; R. Kasturi Rangan

Addresses: Department of Information Science and Engineering, JSS Science and Technology University, Mysuru, India ' Department of Information Science and Engineering, JSS Science and Technology University, Mysuru, India ' Department of Information Science and Engineering, JSS Science and Technology University, Mysuru, India ' Department of Information Science and Engineering, JSS Science and Technology University, Mysuru, India

Abstract: Among the various biometric traits that exist in the human body, sclera is considered to be prominent because of its unique characteristics. In this paper, we propose an improved sclera recognition method using kernel entropy component analysis (KECA). The main objective of this paper is to integrate kernel-based methods with entropy to choose the best principal components. Further, the resulting top principal components are given a symbolic interval valued representation. To evaluate the efficiency of the new proposed representation method, we conducted extensive experimentation using various classifiers. The proposed method has achieved over 5.09% of hike in the accuracy result with 50:50 split and over 10.69% of hike with 60:40 split, respectively. The obtained result of the proposed method is effective and feasible for sclera recognition.

Keywords: sclera; recognition; kernel entropy; symbolic representation.

DOI: 10.1504/IJCVR.2023.130645

International Journal of Computational Vision and Robotics, 2023 Vol.13 No.3, pp.304 - 315

Accepted: 23 Feb 2022
Published online: 02 May 2023 *

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