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Fusing Multi-modal Features for Gesture Recognition
Wu JX(吴家祥); Cheng J(程健); Zhao CY(赵朝阳); Lu HQ(卢汉清)
Conference NameInternational Conference on Multimodal Interface
Conference Date2013-12
Conference PlaceSydney, Australia
AbstractThis paper proposes a novel multi-modal gesture recognition framework and introduces its application to continuous sign language recognition. A Hidden Markov Model is used to construct the audio feature classifier. A skeleton feature classifier is trained to provided complementary information based on the Dynamic Time Warping model. The confidence scores generated by two classifiers are firstly normalized and then combined to produce a weighted sum for the final recognition. Experimental results have shown that the precision and recall scores for 20 classes of our multi-modal recognition framework can achieve 0.8829 and 0.8890 respectively, which proves that our method is able to correctly reject false detection caused by single classifier. Our approach scored 0.12756 in mean Levenshtein distance and was ranked 1st in the Multi-modal Gesture Recognition Challenge in 2013.
Document Type会议论文
Recommended Citation
GB/T 7714
Wu JX,Cheng J,Zhao CY,et al. Fusing Multi-modal Features for Gesture Recognition[C],2013.
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