Grassmann manifold based dynamic hand gesture recognition using depth data

In this paper, we propose a novel Grassmann manifold based framework for dynamic hand gesture recognition from depth data. Automated dynamic hand gesture recognition is important for improving man-machine communication and understanding human behavior. It finds various applications such as human computer interaction, ambient assisted living, automated driver assisted systems. We use depth data or skeleton information to detect the fingertip and store the fingertip points to create the trajectory. In fingertip detection using depth data first we detect the hand using the depth data and used hand shape properties such as finger thickness, finger length, finger width and finger orientation angle to find the shape of the hand. If skeleton data is available we use skeleton information to detect the fingertip in each frame. Then geometrical features are extracted and a unique gesture subspaces created using SVD for each feature vector matrix of each gesture set. These gesture subspaces lie on a Grassmann manifold and capture the intra-class variations and increase the inter-class discriminatory power. We apply Grassmann manifold based discriminant analysis for recognizing each test gesture. We perform experiments on standard datasets and the results show that we have achieved recognition accuracy comparable to the state-of-the-art.