Deep adaptive convolutional neural network for near infrared and thermal face recognition

Dorra El Mahouachi; Moulay A. Akhloufi

doi:10.1117/12.2619242

27 May 2022 Deep adaptive convolutional neural network for near infrared and thermal face recognition

Proceedings Volume 12107, Infrared Technology and Applications XLVIII; 121071R (2022) https://doi.org/10.1117/12.2619242
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Deep Learning algorithms have been widely used for different surveillance tasks in recent years, including people monitoring and counting, abnormal behavior identification, and video segmentation. In most situations, it is assumed that the input images are of high visual quality to provide good performance. When the input data is degraded by variables such as high noise or poor lighting conditions accuracy may degrade. We address the illumination issue in this paper by adapting a face recognition algorithm to near-infrared and thermal images. In this study, we propose a fine-tuning approach to allow deep CNN models to be applied to infrared face recognition (NIR and thermal spectrum). The obtained results with the proposed architecture and infrared images show promising results in deep face recognition with a VAR of 96.68% for the NIR dataset and a VAR of 94.57% for the thermal dataset.

Conference Presentation

Citation Download Citation

Dorra El Mahouachi and Moulay A. Akhloufi "Deep adaptive convolutional neural network for near infrared and thermal face recognition", Proc. SPIE 12107, Infrared Technology and Applications XLVIII, 121071R (27 May 2022); https://doi.org/10.1117/12.2619242

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