Feature Selection for Hand Gesture Recognition in Human-Robot Interaction

Hand gesture recognition has been playing an important role in robotic applications, which allows robots to communicate with humans in an effective way. However, it typically desires to process high-dimensional data, such as images or sensor measurements. To address the computational challenges due to the data growth, it is desirable to select most relevant features during recognition by reducing the redundancy of the data. In this paper, we propose a novel feature selection approach based on the separable nonnegative matrix factorization (NMF) framework for hand gesture recognition. In particular, we adopt a nonconvex regularization term, i.e., the ratio of matrix nuclear norm and Frobenius norm. The proposed method reduces the data dimension by utilizing the data low-rankness in an adaptive way. To address the nonconvexity of the proposed model, we reformulate it by introducing an auxiliary variable and then apply the alternating direction method of multipliers (ADMM). Furthermore, a variety of numerical experiments on binary and grayscale hand gesture images demonstrate the efficiency of the proposed feature selection approach in improving the quality of factorization and its potential impact on robotic applications.