Robust thermal face recognition for law enforcement using optimized deep features with new rough sets-based optimizer

Gaber, Tarek; Nicho, Mathew; Ahmed, Esraa; Hamed, Ahmed

Abstract

In the security domain, the growing need for reliable authentication methods highlights the importance of
thermal face recognition for enhancing law enforcement surveillance and safety especially in IoT applications.
Challenges like computational resources and alterations in facial appearance, e.g., plastic surgery could affect
face recognition systems. This study presents a novel, robust thermal face recognition model tailored for
law enforcement, leveraging thermal signatures from facial blood vessels using a new CNN architecture
(Max and Average Pooling- MAP-CNN). This architecture addresses expression, illumination, and surgical
invariance, providing a robust feature set critical for precise recognition in law enforcement and border control.
Additionally, the model employs the NM-PSO algorithm, integrating neighborhood multi-granulation rough set
(NMGRS) with particle swarm optimization (PSO), which efficiently handles both categorical and numerical
data from multi-granulation perspectives, leading to a 57% reduction in feature dimensions while maintaining
high classification accuracy outperforming ten contemporary models on the Charlotte-ThermalFace dataset by
about 10% across key metrics. Rigorous statistical tests confirm NM-PSO’s superiority, and further robustness
testing of the face recognition model against image ambiguity and missing data demonstrated its consistent
performance, enhancing its suitability for security-sensitive environments with 99% classification accuracy.

• Journal Article Type: Article
• Acceptance Date: Jul 26, 2024
• Online Publication Date: Jul 26, 2024
• Publication Date: 2024-09
• Deposit Date: Sep 6, 2024
• Publicly Available Date: Sep 6, 2024
• Journal: Journal of Information Security and Applications
• Print ISSN: 2214-2126
• Electronic ISSN: 2214-2134
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 85
• Article Number: 103838
• DOI: https://doi.org/10.1016/j.jisa.2024.103838