Boosted subunits: a framework for recognising sign language from videos. Issue 1 (1st February 2013) - Han, Junwei; Awad, George; Sutherland, Alistair
Abstract : This study addresses the problem of vision‐based sign language recognition, which is to translate signs to English. The authors propose a fully automatic system that starts with breaking up signs into manageable subunits. A variety of spatiotemporal descriptors are extracted to form a feature vector for each subunit. Based on the obtained features, subunits are clustered to yield codebooks. A boosting algorithm is then applied to learn a subset of weak classifiers representing discriminative combinations of features and subunits, and to combine them into a strong classifier for each sign. A joint learning strategy is also adopted to share subunits across sign classes, which leads to a more efficient classification. Experimental results on real‐world hand gesture videos demonstrate the proposed approach is promising to build an effective and scalable system.