A tracking framework for accurate face localization

Abstract

This paper proposes a complete framework for accurate face localization on video frames. Detection and forward tracking are first combined according to predefined rules to get a first set of face candidates. Backward tracking is then applied to provide another set of possible localizations. Finally a dynamic programming algorithm is used to select the candidates that minimize a specific cost function. This method was designed to handle different scale, pose and lighting conditions. The experiments show that it improves the face detection rate compared to a frame-based detector and provides a higher precision than a forward information-based tracker.