@article{5c6a01ec269a4c029c99fca3f5bacc9c,
title = "Feature fusion for facial landmark detection",
abstract = "Facial landmark detection is a crucial first step in facial analysis for biometrics and numerous other applications. However, it has proved to be a very challenging task due to the numerous sources of variation in 2D and 3D facial data. Although landmark detection based on descriptors of the 2D and 3D appearance of the face has been extensively studied, the fusion of such feature descriptors is a relatively under-studied issue. In this paper, a novel generalized framework for combining facial feature descriptors is presented, and several feature fusion schemes are proposed and evaluated. The proposed framework maps each feature into a similarity score and combines the individual similarity scores into a resultant score, used to select the optimal solution for a queried landmark. The evaluation of the proposed fusion schemes for facial landmark detection clearly indicates that a quadratic distance to similarity mapping in conjunction with a root mean square rule for similarity fusion achieves the best performance in accuracy, efficiency, robustness and monotonicity.",
keywords = "Facial landmarks, Feature extraction, Feature fusion, Landmark detection",
author = "Panagiotis Perakis and Theoharis Theoharis and Kakadiaris, {Ioannis A.}",
note = "Funding Information: Ioannis A. Kakadiaris is a Hugh Roy and Lillie Cranz Cullen University Professor of Computer Science, Electrical & Computer Engineering, and Biomedical Engineering at the University of Houston. He joined UH in August 1997 after a postdoctoral fellowship at the University of Pennsylvania. Ioannis earned his B.Sc. in Physics at the University of Athens in Greece, his M.Sc. in computer science from Northeastern University and his Ph.D. at the University of Pennsylvania. He is the founder of the Computational Biomedicine Lab ( www.cbl.uh.edu ) and in 2008 he directed the Methodist-University of Houston-Weill Cornell Medical College Institute for Biomedical Imaging Sciences (IBIS, ibis.uh.edu) (position rotates annually among the institutions). His research interests include biometrics, computer vision, and biomedical image analysis. Dr. Kakadiaris is the recipient of a number of awards, including the NSF Early Career Development Award, Schlumberger Technical Foundation Award, UH Computer Science Research Excellence Award, UH Teaching Excellence Award, and the James Muller Vulnerable Plaque Young Investigator Prize. His research has been featured on The Discovery Channel, National Public Radio, KPRC NBC News, KTRH ABC News, and KHOU CBS News. Selected professional service leadership positions include: General Co-Chair of the 2013 Biometrics: Theory, Applications and Systems Conference (BTAS 2013), General Co-chair of the 2014 SPIE Biometric and Surveillance Technology for Human and Activity Identification, Program Co-Chair of the 2015 International Conference on Automatic Face and Gesture Recognition Conference, and Vice-President for Technical Activities for the IEEE Biometrics Council. Funding Information: This research has been co-financed by the European Union (European Social Fund – ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) – Research Funding Program: Heracleitus II. Investing in knowledge society through the European Social Fund. ",
year = "2014",
month = sep,
doi = "10.1016/j.patcog.2014.03.007",
language = "English (US)",
volume = "47",
pages = "2783--2793",
journal = "Pattern Recognition",
issn = "0031-3203",
publisher = "Elsevier Limited",
number = "9",
}