Improving Nighttime Periocular Recognition Performance by Image Superresolution

Recognition of individual identity using the periocular region (i.e., periocular recognition) has emerged as a relatively new modality of biometrics and is a potential substitute for face recognition when facial occlusion happens, e.g., when wearing a mask. Moreover, many application scenarios occur at nighttime, such as nighttime surveillance in law reinforcement. We therefore study the topic of periocular recognition at nighttime using the infrared spectrum. However, the useful and effective area for periocular recognition is quite limited compared to that of face recognition since only the eyes are exposed. As a result, the performance of periocular recognition algorithms is relatively low. This issue of limited area poses a serious challenge even though many state-of-the-art recognition algorithms yield high performance. This situation is even more deteriorated when periocular recognition is performed at nighttime. Thus, we in this paper propose an image super-resolution (SR) based technique for nighttime periocular recognition in which we enlarge the small-sized periocular image to have a larger effective area while retaining a high image quality. Super-resolution of the periocular images is achieved by a CNN model which first conducts interpolation of the periocular area to an expected size and then finds a nonlinear mapping between the input low quality periocular image and the output high quality periocular image. To validate our method, we compare our deep learning-based SR method with the original case of none SR involved at all, as well as the other two cases using traditional methods, namely bilinear interpolation and PCA. In terms of quality metrics such as PSNR and SSIM as well as recognition metrics such as GAR and EER, our method significantly outperforms all the other three methods.