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The recent introduction of novel acquisition devices like the Leap Motion and the Kinect allows to obtain a very informative description of the hand pose that can be exploited for accurate gesture recognition. This paper proposes a novel hand gesture recognition scheme explicitly targeted to Leap Motion data. An ad-hoc feature set based on the positions and(More)
Depth data acquired by current low-cost real-time depth cameras provide a more informative description of the hand pose that can be exploited for gesture recognition purposes. Following this rationale, this paper introduces a novel hand gesture recognition scheme based on depth information. The hand is firstly extracted from the acquired data and divided(More)
Depth data acquired by current low-cost real-time depth cameras provide a very informative description of the hand pose, that can be effectively exploited for gesture recognition purposes. This paper introduces a novel hand gesture recognition scheme based on depth data. The hand is firstly extracted from the acquired depth maps with the aid also of color(More)
Automatic hand gesture recognition plays a fundamental role in current research with the aim of empowering a natural communication between users and virtual reality systems. Starting from an existing work, based on the extraction of two different descriptors from the depth maps followed by their classification with a stand-alone multi SVM classifier, in(More)
Novel 3D acquisition devices like depth cameras and the Leap Motion have recently reached the market. Depth cameras allow to obtain a complete 3D description of the framed scene while the Leap Motion sensor is a device explicitly targeted for hand gesture recognition and provides only a limited set of relevant points. This paper shows how to jointly exploit(More)
In this work an effective face detector based on the well-known Viola-Jones algorithm is proposed. A common issue in face detection is that for maximizing the face detection rate a low threshold is used for classifying as face an input image, but at the same time using a low threshold drastically increases the number of false positives. In this paper(More)
—Depth data acquired by consumer depth cameras provide a very informative description of the hand pose that can be exploited for accurate gesture recognition. A typical hand gesture recognition pipeline requires to identify the hand, extract some relevant features and exploit a suitable machine learning technique to recognize the performed gesture. This(More)
Automatic hand gesture recognition is a challenging problem that is attaining a growing interest due to its applications in natural interfaces for human-machine interaction, automatic sign-language recognition, computer gaming, robotics and healthcare. This chapter briefly reviews existing approaches for automatic hand gesture recognition and proposes a(More)
—Human robot interaction is a very heterogeneous research field and it is attracting a growing interest. A key building block for a proper interaction between humans and robots is the automatic recognition and interpretation of gestures performed by the user. Consumer depth cameras (like MS Kinect) have made possible an accurate and reliable interpretation(More)