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|Title:||A hierarchical Bayesian network for face recognition using 2D and 3D facial data|
|Citation:||IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing, 2015 / Erdogmus, D., Akcakaya, M., Kozat, S., Larsen, J. (ed./s), vol.2015-November, pp.1-6|
|Series/Report no.:||IEEE International Workshop on Machine Learning for Signal Processing|
|Conference Name:||IEEE International Workshop on Machine Learning for Signal Processing (17 Sep 2015 - 20 Sep 2015 : Boston, NY)|
|Abstract:||In this paper, we tackle the problem of face classification and verification. We present a novel face representation method based on a Bayesian network. The model captures dependencies between 2D salient facial regions and the full 3D geometrical model of the face, which makes it robust to pose variations, and useable in unconstrained environments. We present experiments on the challenging databases FERET and LFW, which show a significant advantage over state-of-the-art methods.|
|Appears in Collections:||Aurora harvest 3|
Computer Science publications
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