Automatic gesture recognition has received much attention due to its potential in various applications. In this paper, we successfully apply an evolutionary method-genetic programming (GP) to synthesize machine learned spatio-temporal descriptors for automatic gesture recognition instead of using hand-crafted descriptors. In our architecture, a set of primitive low-level 3D operators are first randomly assembled as tree-based combinations, which are further evolved generation-by-generation through the GP system, and finally a well performed combination will be selected as the best descriptor for high-level gesture recognition. To the best of our knowledge, this is the first report of using GP to evolve spatio-temporal descriptors for gesture recognition. We address this as a domain-independent optimization issue and evaluate our proposed method, respectively, on two public dynamic gesture datasets: Cambridge hand gesture dataset and Northwestern University hand gesture dataset to demonstrate its generalizability. The experimental results manifest that our GP-evolved descriptors can achieve better recognition accuracies than state-of-the-art hand-crafted techniques.
|Title of host publication
|10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2013
|Published - 15 Jul 2013