MobileNetV3 for Secure Biometrics Lightweight Facial Recognition with SHAP-Driven Insights

DOI

10.2991/978-94-6463-718-2_138How to use a DOI?

Keywords

Enhanced Multi-Modal Biometric Authentication; Deep Learning; Explainable AI; Data Fusion; Privacy Preservation; Anti-Spoofing; False Positives

Abstract

This research work presents a new multi-modal biometric security management system using Deep Learning with Explainable Ai (XAI) to improve the security of different authentication processing. The framework aims to achieve the best possible performance across the entire system, such as accuracy, scalability and latency, by taking advantage of multiple biometric modalities face recognition, fingerprint scanning, voice authentication and iris recognition. These novel approaches encompass privacy-friendly encryption methods for secure data handling, deep learning strategems for efficient data integration, and on-demand performance upgrades as a response against anti-spoofing tactics, addressing the central obstacles of privacy, data fusion, anti-spoofing counteraction, and generalization. Also, use of explainable AI in intelligent process automation assists with building confidence and trust over the decisions made. The suggested framework additionally addresses false positive/negative concerns and fairness and the capability to minimize discrimination across different demographic groups. For another its resistance to various environments such as lighting variation, background noise etc. provide its utility in real life applications. Overall, it allows for a smooth user journey while also offering a flexible solution for multi-modal biometric authentication across sectors including secure access, financial endeavours, and public safety.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - P. Vasuki
AU  - M. K. Nivodhini
AU  - R. Banupriya
AU  - R. Prithiv Raj
AU  - N. Sakthivel Rajan
AU  - S. Rishikesh Jishnuvel
PY  - 2025
DA  - 2025/05/23
TI  - MobileNetV3 for Secure Biometrics Lightweight Facial Recognition with SHAP-Driven Insights
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1655
EP  - 1669
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_138
DO  - 10.2991/978-94-6463-718-2_138
ID  - Vasuki2025
ER  -