Super-Resolution for Selfie Biometrics: Introduction and Application to
Face and Iris
- URL: http://arxiv.org/abs/2204.05688v1
- Date: Tue, 12 Apr 2022 10:28:31 GMT
- Title: Super-Resolution for Selfie Biometrics: Introduction and Application to
Face and Iris
- Authors: Fernando Alonso-Fernandez, Reuben A. Farrugia, Julian Fierrez, Josef
Bigun
- Abstract summary: Lack of resolution has a negative impact on the performance of image-based biometrics.
Super-resolution techniques have to be adapted for the particularities of images from a specific biometric modality.
This chapter presents an overview of recent advances in super-resolution reconstruction of face and iris images.
- Score: 67.74999528342273
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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