Feature-Fusion Guidelines for Image-Based Multi-Modal Biometric Fusion
DOI:
https://doi.org/10.18489/sacj.v29i1.436Abstract
The feature level, unlike the match score level, lacks multi-modal fusion guidelines. This work demonstrates a new approach for improved image-based biometric feature-fusion. The approach extracts and combines the face, fingerprint and palmprint at the feature level for improved human identification accuracy. Feature-fusion guidelines, proposed in our recent work, are extended by adding a new face segmentation method and the support vector machine classifier. The new face segmentation method improves the face identification equal error rate (EER) by 10%. The support vector machine classifier combined with the new feature selection approach, proposed in our recent work, outperforms other classifiers when using a single training sample. Feature-fusion guidelines take the form of strengths and weaknesses as observed in the applied feature processing modules during preliminary experiments. The guidelines are used to implement an effective biometric fusion system at the feature level, using a novel feature-fusion methodology, reducing the EER of two groups of three datasets namely: SDUMLA face, SDUMLA fingerprint and IITD palmprint; MUCT Face, MCYT Fingerprint and CASIA Palmprint.Downloads
Published
2017-07-08
Issue
Section
Research Papers (general)
License
Copyright of all work published here subsists in the authors. While SACJ retains right of first publication, subsequent re-publication is expressly permitted provided the original SACJ publication is acknowledged and cited, according to the terms detailed below. If plagiarism is detected during review, a paper may be summarily rejected and will not be accepted unless even minor infringements are corrected. Should plagiarism be detected after a paper is published, the Editor reserves the right to withdraw a paper from publication. We expect authors to be honest in representing work as their own, and to respect the time and effort our reviewers put in without an undue burden of policing plagiarism, and hence take violations seriously. SACJ applies the Creative Commons Attribution NonCommercial 4.0 License (CC BY-NC 4.0) to all papers published in this journal. Authors who publish with SACJ agree to the following:- Authors retain copyright and grant SACJ right of first publication. The work is additionally licensed under a Creative Commons Attribution Non-Commercial License that requires others who share the work to acknowledge the work’s authorship and initial publication in SACJ. Should anyone else wish to make commercial use of the work, SACJ cedes the right to the author to negotiate terms and does not expect to be paid any royalties.
- Authors may enter into additional arrangements for non-exclusive distribution of the SACJ-published version of the work (e.g., post it to a repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are required to refrain from posting their work online prior to completion of reviews so as not to compromise double-blind reviewing or confuse plagiarism checks.
