Real-time hand posture recognition using hand geometric features and Fisher Vector

Abstract Hand posture recognition (HPR), one of the most effective and intuitive human computer interfaces, has been extensively studied and widely adopted in various multimedia applications. Shape descriptors extracted from a hand contour or silhouette have been proved effective in representing a hand posture. However, it is difficult for these shape-based methods to achieve good balance between accuracy and efficiency. To this end, in this paper, we propose a novel hand shape descriptor based on a set of geometric features (SoGF) and Fisher Vector (FV), for effective and efficient HPR. Three types of geometric features, including distances, angles and curvatures, are extracted from a hand silhouette to form a discriminative local descriptor, and FV is adopted to encode the set of local descriptors for compact hand shape representation. To recognize hand postures, we construct a classifier using a multi-class Support Vector Machine (SVM) with FVs as input. The experimental results on four public HPR datasets show that the proposed method can achieve the mean accuracy of the state-of-the-art methods in real time.