Automated User Identification from Facial Thermograms with Siamese Networks
This work addresses biometric security systems, but it is incremental as it builds on existing thermal imaging and Siamese network methods.
The paper tackled user identification from facial thermograms by proposing Siamese neural networks, achieving approximately 80% accuracy on a proprietary dataset.
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.