Abstract
The hand is an important part of the body used to express information through gestures, and its movements can be used in dynamic gesture recognition systems based on computer vision with practical applications, such as medical, games and sign language. Although depth sensors have led to great progress in gesture recognition, hand gesture recognition still is an open problem because of its complexity, which is due to the large number of small articulations in a hand. This paper proposes a novel approach for hand gesture recognition with depth maps generated by the Microsoft Kinect Sensor (Microsoft, Redmond, WA, USA) using a variation of the CIPBR (convex invariant position based on RANSAC) algorithm and a hybrid classifier composed of dynamic time warping (DTW) and Hidden Markov models (HMM), called the hybrid approach for gesture recognition with depth maps (HAGR-D). The experiments show that the proposed model overcomes other algorithms presented in the literature in hand gesture recognition tasks, achieving a classification rate of 97.49% in the MSRGesture3D dataset and 98.43% in the RPPDI dynamic gesture dataset.
Highlights
Gestures and hand postures have been used for a long time as a way to express feelings and to communicate information between people
We propose a novel approach for dynamic gesture recognition with depth maps, called the hybrid approach for gesture recognition with depth maps (HAGR-D)
There are 12 dynamic hand gestures defined by American Sign Language (ASL) in MSRGesture3D, and each dynamic gesture was performed two or three times by each one of 10 subjects
Summary
Gestures and hand postures have been used for a long time as a way to express feelings and to communicate information between people. A gesture can represent a simple action, such as to allow people to cross a street, or complex body expressions belonging to a specific population language. Sign language uses both hand and body postures instead of sound patterns to establish communication. The first category uses sensors, such as wearable devices with accelerometers and markers, to capture a gesture and its corresponding movement. This invasive technology limits the normal execution of the gesture
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