Landmarks are unique points that can be located on every face. Facial landmarks typically recognized by people are correlated with anthropomorphic points. Our purpose is to employ in 3D face recognition such landmarks that are easy to interpret. Face understanding is construed as identification of face characteristic points with automatic labeling of them. In this paper, we apply methods based on Self Organizing Maps to understand 3D faces.
Self Organizing Maps for 3D Face Understanding / Starczewski, J; Pabiasz, S; Vladymyrska, N; Marvuglia, A; Napoli, Christian; Woźniak, M.. - 9693:(2016), pp. 210-217. (Intervento presentato al convegno 15th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2016 tenutosi a Zakopane; Poland) [10.1007/978-3-319-39384-1_19].
Self Organizing Maps for 3D Face Understanding
NAPOLI, CHRISTIAN;
2016
Abstract
Landmarks are unique points that can be located on every face. Facial landmarks typically recognized by people are correlated with anthropomorphic points. Our purpose is to employ in 3D face recognition such landmarks that are easy to interpret. Face understanding is construed as identification of face characteristic points with automatic labeling of them. In this paper, we apply methods based on Self Organizing Maps to understand 3D faces.| File | Dimensione | Formato | |
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