Fusing Multi-modal Features for Gesture Recognition

	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)
	2013-12
会议名称	International Conference on Multimodal Interface
会议日期	2013-12
会议地点	Sydney, Australia
摘要	This 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.
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/14971
专题	紫东太初大模型研究中心_图像与视频分析
作者单位	中国科学院自动化研究所
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Wu JX,Cheng J,Zhao CY,et al. Fusing Multi-modal Features for Gesture Recognition[C],2013.

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