Experimental Analysis of Face and Iris Biometric Traits Based on the Fusion Approach

C Sapna Kumari; K P Nagapushpa; H Jayalaxmi; C N Asha; Sunil S Harakannanavar; Jagadish S Jakati    nbsp

doi:10.17485/IJST/v16i31.314

Article

Experimental Analysis of Face and Iris Biometric Traits Based on the Fusion Approach

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i31.314

Year: 2023, Volume: 16, Issue: 31, Pages: 2388-2397

Original Article

Experimental Analysis of Face and Iris Biometric Traits Based on the Fusion Approach

C Sapna Kumari^1*, K P Nagapushpa², H Jayalaxmi², C N Asha², Sunil S Harakannanavar¹, Jagadish S Jakati³

¹Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Yelahanka, Bangalore, Karnataka, India
²Department of Electronics and Communication Engineering, Acharya Institute of Technology, Bangalore, Karnataka, India
³Assistant Professor, Department of Electronics and Telecommunication Engineering, Zeal College of Engineering & Research, SPP University, Narhe, Pune, 411 041, Maharashtra, India

*Corresponding Author
Email: [email protected]

Received Date:12 February 2023, Accepted Date:07 July 2023, Published Date:14 August 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives : To develop an efficient algorithm for face and iris multimodal traits on ORL and CASIA dataset and to increase the performance rate and decrease the error rate of the model. The main goal is to increase the performance rate and decrease the error rate of the model. Methods: The proposed algorithm utilizes a fusion of face and iris modalities using Stationary Wavelet Transform (SWT) and Local Binary Pattern (LBP) techniques. The Principal Component Analysis (PCA) is applied to reduce the dimensionality of each sample, improving efficiency while preserving the most relevant information. The relevant characteristics from both face and iris modalities are fused to create a comprehensive pattern for an individual. Findings: The obtained features are compared with the features of the database images using a Euclidean Distance classifier. The performance of the proposed model is evaluated using the ORL and CASIA iris datasets. The accuracy achieved by the proposed algorithm is 99.42%, demonstrating robustness. Novelty: The algorithm introduces feature-level fusion, combining the characteristics of both face and iris modalities. The model encompasses the training and recognition phases within a biometric system. During the training phase, the biometric modality is captured and processed using the fusion of SWT+LBP+PCA techniques to form a template for each user. These templates are later stored in the database for recognition purposes.

Keywords: Biometrics; Trait; Face; Iris; Multimodal; Stationary Wavelet Transform

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Copyright

© 2023 Kumari et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)