Gesture recognition by model matching of slope difference distribution features

Gesture recognition has been studied for many decades and remains an open problem. One possible reason is that the features extracted for gesture recognition are not effective enough. A trained person could recognize a variety of gestures robustly based on the contour of the hand. Accordingly, there must be a machine vision method that could recognize the same variety of gestures robustly based on the shape features. The key technique lies in how to define and extract the shape features effectively. In this paper, we propose to recognize of the gestures with the shape features defined and extracted by slope difference distribution (SDD). To calculate the SDD features, onedimensional hand contour is defined as the distance distribution between the hand centroid and each point on the two-dimensional hand contour. To make the model matching robust, only the common intra-class SDD features are selected as the sparse representation of the hand contour. The proposed gesture recognition method was tested with three public datasets and achieved state-of-the-art recognition accuracy on two public datasets.