Fuzzy-based latent-dynamic conditional random fields for continuous gesture recognition

We show an original method for automatic hand gesture recognition that makes use of fuzzified latent-dynamic conditional random fields (LDCRF). In this method, fuzzy linguistic variables are used to model the features of hand gestures and then to modify the potential function in LDCRFs. By combining LDCRFs and fuzzy sets, these fuzzy-based LDCRFs (FLDCRF) have the advantages of LDCRFs in sequence labeling along with the advantage of retaining the imprecise character of gestures. The efficiency of the proposed method was tested with unsegmented gesture sequences in three different hand gesture data sets. The experimental results demonstrate that FLDCRFs compare favorably with support vector machines, hidden conditional random fields, and LDCRFs on hand gesture recognition tasks.

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