ANYRES: Generating High-Resolution visible-face images from Low-Resolution thermal-face images

Cross-spectral Face Recognition (CFR) aims to compare facial images across different modalities, i.e., the visible and thermal spectra. CFR is more challenging than traditional face recognition (FR) due to the profound modality gap in-between spectra. As related applications range from nightvision FR to robust presentation attacks detection, acquisition involves capturing images at various distances, represented by different image resolutions. Prior approaches have addressed CFR by considering a fixed resolution, necessitating that a subject stands at a precise distance from a given sensor during acquisition, which constitutes an impractical scenario in real-life. Towards loosening this constraint, we propose ANYRES, a unified model endowed with the ability to handle a wide range of input resolutions. ANYRES generates high resolution visible images from low resolution thermal images, placing emphasis on maintaining the cross-spectral identity. We demonstrate the effectiveness of the method and present extensive FR experiments on multi-spectral paired face datasets.