  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.
