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A Statistical-Topological Feature Combination for Recognition of Isolated Hand Gestures from Kinect Based Depth Images

Abstract : Reliable hand gesture recognition is an important problem for automatic sign language recognition for the people with hearing and speech disabilities. In this paper, we create a new benchmark database of multi-oriented, isolated ASL numeric images using recently launched Kinect V2. Further, we design an effective statistical-topological feature combinations for recognition of the hand gestures using the available V1 sensor dataset and also over the new V2 dataset. For V1, our best accuracy is 98.4% which is comparable with the best one reported so far and for V2 we achieve an accuracy of 92.2% which is first of its kind.
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https://hal.inria.fr/hal-01517817
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Submitted on : Wednesday, May 3, 2017 - 4:35:05 PM
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Long-term archiving on: : Friday, August 4, 2017 - 1:23:34 PM

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Soumi Paul, Hayat Nasser, Mita Nasipuri, Phuc Ngo, Subhadip Basu, et al.. A Statistical-Topological Feature Combination for Recognition of Isolated Hand Gestures from Kinect Based Depth Images. IWCIA, Jun 2017, Plovdiv, Bulgaria. ⟨10.1007/978-3-319-59108-7⟩. ⟨hal-01517817⟩

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