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 pos-sible 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.
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© 2006 International Federation for Information Processing
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Cherif, I., Solachidis, V., Pitas, I. (2006). A Tracking Framework for Accurate Face Localization. In: Bramer, M. (eds) Artificial Intelligence in Theory and Practice. IFIP AI 2006. IFIP International Federation for Information Processing, vol 217. Springer, Boston, MA . https://doi.org/10.1007/978-0-387-34747-9_40
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DOI: https://doi.org/10.1007/978-0-387-34747-9_40
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-34654-0
Online ISBN: 978-0-387-34747-9
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