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3D hand gesture recognition has attracted increasing research interests in computer vision, pattern recognition and human-computer interaction. The emerging depth sensors greatly inspired various hand gesture recognition approaches and applications, which were severely limited in the 2D domain with conventional cameras. This paper presents a survey of some recent works on hand gesture recognition using 3D depth sensors. We first review the commercial depth sensors and public datasets which are widely used in this field. Then, we review the state-of-the-art research for 3D hand gesture recognition in four aspects: 3D hand modeling, static hand gesture recognition, hand trajectory gesture recognition and continuous hand gesture recognition. While the emphasis is on 3D hand gesture recognition approaches, the related applications and typical systems are also briefly summarized for practitioners.

Abstract-This paper presents an extreme learning machine (ELM) based control scheme for uncertain robot manipulators to perform haptic identification. ELM is used to compensate for the unknown nonlinearity in the manipulator dynamics. The ELM enhanced controller ensures that the closed-loop controlled manipulator follows a specified reference model, in which the reference point as well as the feedforward force is adjusted after each trial for haptic identification of geometry and stiffness of an unknown object. A neural learning law is designed to ensure finite-time convergence of the neural weight learning, such that exact matching with the reference model can be achieved after the initial iteration. The usefulness of the proposed method is tested and demonstrated by extensive simulation studies.

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The recognition of multi-person interactions still remains a challenge because of the mutual occlusion and redundant poses. We propose an interactive body part contrast mining method based on joints for human interaction recognition. To efficiently describe interactions, we propose an interactive body part model which connects the interactive limbs of different participants to represent the relationship of interactive body parts. Then we calculate the spatial-temporal joint features for 8 interactive limb pairs in a short frame set for motion description (poselets). Employing contrast mining, we determine the essential interactive pairs and poselets for each interaction class to delete the redundant action information, and use these poselets to generate a poselet dictionary for interaction representation following bag-of-words. SVM with RBF kernel is adopted for recognition. We evaluate the proposed algorithm on two databases, the SBU interaction database and a newly collected RGBD-skeleton interaction database. Experiment results indicate the effectiveness of the proposed algorithm. The recognition accuracy reaches 85.4% on our interaction database, and 86.8% on SBU interaction database, 6% higher than the method in [1].

Inspired by practical importance of social networks, economic networks, biological networks and so on, studies on large and complex networks have attracted a surge of attentions in the recent years. Link prediction is a fundamental issue to understand the mechanisms by which new links are added to the networks. We introduce the method of robust principal component analysis (robust PCA) into link prediction, and estimate the missing entries of the adjacency matrix. On one hand, our algorithm is based on the sparsity and low rank property of the matrix, on the other hand, it also performs very well when the network is dense. This is because a relatively dense real network is also sparse in comparison to the complete graph. According to extensive experiments on real networks from disparate fields, when the target network is connected and sufficiently dense, whatever it is weighted or unweighted, our method is demonstrated to be very effective and with prediction accuracy being considerably improved comparing with many state-of-the-art algorithms.

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