Making long-wave infrared face recognition robust against image quality degradations

Face identification systems that operate on long wave infrared (LWIR) images are able to overcome some of the limitations of approaches based on visible images, such as dealing effectively with illumination variations. Nonetheless, distortions of perceptual image quality can impair the performance of thermal face recognition systems. Although the interaction between perceptual image quality and tasks such as face detection has been studied on visual images, the development of similar models has not been applied to the LWIR-based face recognition problem. Here, we analyze the impact of four common infrared image distortions (gaussian noise, blur, non-uniformity, and JPEG compression) on two thermal face recognition systems. We propose an LWIR image face recognition framework, based on thermal signature templates, and enhanced by natural scene statistics image quality descriptors, which achieves system robustness against image quality distortions. Furthermore, we develop a novel infrared face recognition system that is based on the complex wavelet structural similarity (CW-SSIM) index, which exhibits resistance to image distortions, within a relatively simple implementation. Our results validate the applicability of image quality assessment models to biometric tasks on LWIR images. To facilitate our study, we created two new LWIR facial image databases, with different poses, expressions and illumination conditions.