International Journal on Science and Technology

E-ISSN: 2229-7677   •   Impact Factor: 9.88

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 16 Issue 2 April-June 2025 Submit your research before last 3 days of June to publish your research paper in the issue of April-June.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 16 Issue 2
April-June 2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

deep learning approach for multimodal biometric recognition system based on face, iris and finger vein traits

Author(s) K. Nagamani, K Geethika, P Snehith, G Manas
Country India
Abstract Biometric recognition systems have become an essential component of modern authentication mechanisms. However, unimodal biometric systems relying on a single trait, such as face or iris, are vulnerable to spoofing attacks, environmental variations, and acquisition noise. To overcome these limitations, this study proposes a deep learning-based multimodal biometric recognition system that integrates face, iris, and finger vein traits. The system utilizes Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) to extract deep feature representations from each biometric modality. A feature-level fusion approach is employed to combine the extracted features, leveraging the unique strengths of each modality to enhance recognition accuracy and robustness. Experimental evaluations on benchmark biometric datasets demonstrate that the
proposed multimodal system significantly outperforms unimodal biometric models in terms of accuracy, security, and resilience against spoofing attacks. Additionally, the hybrid feature and score-level fusion strategy ensures lower false acceptance and rejection rates, making the system more reliable for real-world applications such as access control, financial security, and identity
verification. The results highlight the potential of deep learning in advancing multimodal biometric systems, reinforcing authentication security while minimizing vulnerabilities. This research establishes a strong foundation for future innovations in biometric security and identity management.
Keywords NLP, SVM, LIWC, LDA, MLP, Depression, Detection, Linguistic patterns.
Field Engineering
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-06-03
DOI https://doi.org/10.71097/IJSAT.v16.i2.5843
Short DOI https://doi.org/g9m288
View / Download PDF File

Share this


CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJSAT DOI prefix is
10.71097/IJSAT

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 15 Isu 4Cover Page Vol 15 Isu 3Cover Page Vol 15 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJSAT
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us Message on WhatsApp +91-9687-182-185 editor@ijsat.org