A multi-class hand gesture recognition in complex background using Sequential minimal Optimization

This paper presents a novel approach for hand gesture recognition in complex background images. The method is based on Histograms of Orientation Gradient (HOG) which in independent of segmentation task followed by Sequential minimal Optimization (SMO). In our experiment we use benchmark Jochen-Triesch database for hand gesture recognition under complex and clutter background. In addition to this perturbation is added to images to increase the database. The vector size is reduced by increasing the number of pixels per cell without compromising accuracy. The proposed system gives overall recognition rate of 93.12% which demonstrates the robustness of the system under illumination changes, rotation and translation.