Towards Robust Facial Recognition: Gabor Filter-Based Feature Extraction for NIR-VIS Heterogeneous Face Recognition
João V. R. de Andrade, Agostinho Freire, Guilherme C. Pereira, Cristian Millán-Arias, Bruno Fernandes, Carmelo J. A. Bastos-Filho, Jorge Tortato, Luiz Gustavo Schitz da Rocha, Alexandre Magno Andrade Maciel · 2025
Face recognition systems have advanced significantly in recent years, spurred by computational improvements that have enabled the development of robust deep-learning models. Nevertheless, some challenges persist in real-world applications, such as variations in illumination conditions. Near-infrared (NIR) cameras have been addressed as a possible solution to mitigate this problem. However, most face recognition systems are trained on visible spectrum (VIS) image datasets, necessitating cross-domain mapping strategies for deploying such cameras. This work introduces a novel training strategy for enhancing NIR-VIS Heterogeneous Face Recognition (HFR) systems, considering illumination variance and dataset limitations. We propose integrating Gabor filters, which are applied to extract invariant features from VIS images for use in the NIR domain, involving Principal Component Analysis (PCA) for feature reduction and employing the Mahalanobis distance for classification. This method aims to improve the robustness and accuracy of facial recognition across diverse lighting conditions. We demonstrate significant performance improvements on the CASSIA NIR-VIS 2.0, CARL and BUAAVISNIR datasets when applying Gabor filters. We measured enhanced model performance by up to 76% of the Rank-five metric, in the best scenario, with compromising only 9% (in the worst scenario) of its results in VIS scenarios. The proposal maintained a comparable execution time to the traditional model without the Gabor Filter feature extraction step, adding only, in the worst case, a minimal overhead of 13 milliseconds for the architecture GhostFaceNet using the Gabor filtering process. These results suggest that the proposed approach is feasible for real-world applications, especially in environments with limited computational resources.