Recognition of signed expressions using visually-Oriented subunits obtained by an immune-Based optimization

The paper considers automatic visual recognition of signed expressions. The proposed method is based on modeling gestures with subunits, which is similar to modeling speech by means of phonemes. To define the subunits a data-driven procedure is applied. The procedure consists in partitioning time series, extracted from video, into subsequences which form homogeneous groups. The cut points are determined by an immune optimization procedure based on quality assessment of the resulting clusters. In the paper the problem is formulated, its solution method is proposed and experimentally verified on a database of 100 Polish words. The results show that our subunit-based classifier outperforms its whole-word-based counterpart, which is particularly evident when new words are recognized on the basis of a small number of examples.