Low-Quality Face Recognition in the Wild

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    Although face recognition systems have achieved impressive performance in recent years, the low-resolution face recognition (LRFR) task remains challenging, especially when the LR faces are captured under non-ideal conditions, as is common in surveillance-based applications.  Faces captured in such conditions are often contaminated by blur, nonuniform lighting, and nonfrontal face pose.  In this project, we are aiming at analyzing face recognition techniques using data captured under low-quality conditions in the wild. The performance gap between general unconstrained face recognition and face recognition in surveillance veidos is shown and new algorithms are designed to tackle this specific problem.

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