Abstract
The lack of resolution has a negative impact on the performance of image-based biometrics. Many applications which are becoming ubiquitous in mobile devices do not operate in a controlled environment, and their performance significantly drops due to the lack of pixel resolution. While many generic super-resolution techniques have been studied to restore low-resolution images for biometrics, the results obtained are not always as desired. Those generic methods are usually aimed to enhance the visual appearance of the scene. However, producing an overall visual enhancement of biometric images does not necessarily correlate with a better recognition performance. Such techniques are designed to restore generic images and therefore do not exploit the specific structure found in biometric images (e.g. iris or faces), which causes the solution to be sub-optimal. For this reason, super-resolution techniques have to be adapted for the particularities of images from a specific biometric modality. In recent years, there has been an increased interest in the application of super-resolution to different biometric modalities, such as face iris, gait or fingerprint. This chapter presents an overview of recent advances in super-resolution reconstruction of face and iris images, which are the two prevalent modalities in selfie biometrics. We also provide experimental results using several state-of-the-art reconstruction algorithms, demonstrating the benefits of using super-resolution to improve the quality of face and iris images prior to classification. In the reported experiments, we study the application of super-resolution to face and iris images captured in the visible range, using experimental setups that represent well the selfie biometrics scenario.
Abstract (translated)
URL
https://arxiv.org/abs/2204.05688
