Automated User Identification from Facial Thermograms with Siamese Networks

TL;DR

This paper explores using thermal facial images and Siamese neural networks for biometric identification, comparing spectral ranges, defining camera requirements, and achieving around 80% accuracy on proprietary data.

Contribution

It introduces a Siamese network approach for thermal facial recognition and analyzes spectral ranges and camera specifications for biometric systems.

Findings

Achieved approximately 80% accuracy on proprietary dataset.

Siamese networks are effective for thermal biometric identification.

Hybrid visible-infrared systems can enhance recognition reliability.

Abstract

The article analyzes the use of thermal imaging technologies for biometric identification based on facial thermograms. It presents a comparative analysis of infrared spectral ranges (NIR, SWIR, MWIR, and LWIR). The paper also defines key requirements for thermal cameras used in biometric systems, including sensor resolution, thermal sensitivity, and a frame rate of at least 30 Hz. Siamese neural networks are proposed as an effective approach for automating the identification process. In experiments conducted on a proprietary dataset, the proposed method achieved an accuracy of approximately 80%. The study also examines the potential of hybrid systems that combine visible and infrared spectra to overcome the limitations of individual modalities. The results indicate that thermal imaging is a promising technology for developing reliable security systems.