Face Image Quality Enhancement Study for Face Recognition
This addresses face recognition accuracy issues for biometric and computer vision researchers, but is incremental as it builds on existing enhancement methods.
The paper tackles face recognition in low-quality photos by assembling a large database and testing state-of-the-art enhancement approaches, resulting in a new protocol that reveals challenging aspects of the problem.
Unconstrained face recognition is an active research area among computer vision and biometric researchers for many years now. Still the problem of face recognition in low quality photos has not been well-studied so far. In this paper, we explore the face recognition performance on low quality photos, and we try to improve the accuracy in dealing with low quality face images. We assemble a large database with low quality photos, and examine the performance of face recognition algorithms for three different quality sets. Using state-of-the-art facial image enhancement approaches, we explore the face recognition performance for the enhanced face images. To perform this without experimental bias, we have developed a new protocol for recognition with low quality face photos and validate the performance experimentally. Our designed protocol for face recognition with low quality face images can be useful to other researchers. Moreover, experiment results show some of the challenging aspects of this problem.