Low-resolution video face recognition with face normalization and feature adaptation

Face analysis is a challenging topic, especially when addressing low-resolution data. While face detection is working satisfactorily on such data, further facial analysis often struggles. We specifically address the issues of face registration, face normalization and facial feature extraction to perform low-resolution face recognition. For face registration, an approach for landmark detection, pose estimation and pose normalization is presented. In addition, a strategy to mirror the visible face half in the case of a rotated face is suggested. Next, the normalized face is used to extract the features for recognition. Using situation adapted local binary patterns (LBP) which are collected according to the proposed framework, including several scales and spatial overlaps, boosts the recognition performance well above the baseline. Results are presented on the YouTube Faces Database which is the current state-of-the-art dataset for video face recognition. Proper adjustments are made to convert this high-resolution dataset to a low-resolution one. We show that the presented adaptations increase face recognition performance significantly for low-resolution scenarios, closing a large part of the gap to high resolution face recognition.