A Dempster-Shafer Theory Based Combination of Classifiers for Hand Gesture Recognition

Thomas Burger (1, 2) , Oya Aran (3) , Alexandra Urankar (2) , Alice Caplier (1) , Lale Akarun (3, 4)

1 GIPSA-GPIG - GIPSA - Géométrie, Perception, Images, Geste
2 FT R&D - France Télécom Recherche & Développement
3 Department of Computer Engineering [Bogazici]
4 Boǧaziçi üniversitesi = Boğaziçi University [Istanbul]

As part of our work on hand gesture interpretation, we present our results on hand shape recognition. Our method is based on attribute extraction and multiple partial classifications. The novelty lies in the fashion the fusion of all the partial classification results are performed. This fusion is (1) more efficient in terms of information theory and leads to more accurate results, (2) general enough to allow heterogeneous sources of information to be taken into account: Each classifier output is transformed to a belief function, and all the corresponding functions are fused together with other external evidential sources of information.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Thomas Burger, Oya Aran, Alexandra Urankar, Alice Caplier, Lale Akarun. A Dempster-Shafer Theory Based Combination of Classifiers for Hand Gesture Recognition. Communications in Computer and Information Science, 2009, Computer Vision and Computer Graphics. Theory and Applications, 21, pp.137-150. ⟨10.1007/978-3-540-89682-1_10⟩. ⟨hal-00365853⟩