Sparse representation based approach for RGB-D hand gesture recognition

Abstract

In this paper, we present a new algorithm for RGB-D hand gesture recognition by using multi-attribute sparse representation enforced with group constraints. Firstly, the hand region is segmented from the background according to the depth information. Then, we process all gesture-performing hand region images with PCA to reduce the feature dimension. To obtain a more accurate and discriminative representation, a multi-attribute sparse representation is employed for hand gesture recognition from different view angles. The multiple attributes for a gesture image can be represented by individual binary matrices to indicate the group properties for each gesture. Then, these attribute matrices are incorporated into the formulation of l1-minimization in the sparse coding framework. Finally, the effectiveness and robustness of the proposed method are demonstrated through experiments on a public RGB-D hand gesture dataset.