This paper is published in Volume-6, Issue-1, 2020
Area
Deep Learning
Author
Harsha. N
Co-authors
Dr. Balamurugan.A
Org/Univ
Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
Pub. Date
31 January, 2020
Paper ID
V6I1-1161
Publisher
Keywords
Biometrics, Periocular Recognition, Extraction, Bayesian support vector machine, and Harlick features

Citationsacebook

IEEE
Harsha. N, Dr. Balamurugan.A. Enhancing periocular recognition using Bayesian support vector machine attractive for recognition, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Harsha. N, Dr. Balamurugan.A (2020). Enhancing periocular recognition using Bayesian support vector machine attractive for recognition. International Journal of Advance Research, Ideas and Innovations in Technology, 6(1) www.IJARIIT.com.

MLA
Harsha. N, Dr. Balamurugan.A. "Enhancing periocular recognition using Bayesian support vector machine attractive for recognition." International Journal of Advance Research, Ideas and Innovations in Technology 6.1 (2020). www.IJARIIT.com.

Abstract

Periocular recognition has been an active research area in the field of biometrics. The periocular region is normally a rectangular region localized by the eye center or the inner and outer corners of the eye. Choosing features that represent the reliable and discriminative properties of the periocular region is one of the most critical tasks in the periocular recognition problem. This project tackles this feature extraction problem and proposes a novel approach to efficiently extract discriminative properties of the periocular region with high recognition performance. The proficiency to learn robust features from the images makes the Bayesian support vector machine (BSVM) attractive for recognition. Harlick features and edge histogram descriptor is used to extract the features of training images.
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