Robust Face Recognition Strategies Using Feed-Forward Architectures and Parts

摘要

This paper describes new feed-forward architectural and configural/holistic strategies for robust face recognition. This includes adaptive and robust correlation filters that lock on both appearance and location, and recognition-by-parts using boosting over strangeness driven weak learners. The utility of the proposed architectural strategies, shown with respect to different databases, includes occlusion, disguise, and temporal changes. The results obtained confirm and complement key findings on the ways people recognize each other, among them that the facial features are processed holistically and that the eyebrows are among the most important features for recognition.