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This paper presents the state-of-the art dynamic sign language recognition (DSLR) system for smart home interactive applications. Our novel DSLR system comprises two main subsystems: an image processing (IP) module and a stochastic linear formal grammar (SLFG) module. Our IP module enables us to recognize the individual words of the sign language (i.e., a single gesture). In this module, we used the bag-of-features (BOFs) and a local part model approach for bare hand dynamic gesture recognition from a video. We used dense sampling to extract local 3-D multiscale whole-part features. We adopted 3-D histograms of a gradient orientation descriptor to represent features. The k-means++ method was applied to cluster the visual words. Dynamic hand gesture classification was conducted using the BOFs and nonlinear support vector machine methods. We used a multiscale local part model to preserve temporal context. The SLFG module analyzes the sentences of the sign language (i.e., sequences of gestures) and determines whether or not they are syntactically valid. Therefore, the DSLR system is not only able to rule out ungrammatical sentences, but it can also make predictions about missing gestures, which, in turn, increases the accuracy of our recognition task. Our IP module alone seals the accuracy of 97% and outperforms any existing bare hand dynamic gesture recognition system. However, by exploiting syntactic pattern recognition, the SLFG module raises this accuracy by 1.65%. This makes the aggregate performance of the DSLR system as accurate as 98.65%.
Abid et al. (Wed,) studied this question.