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

Soumi Paul 1 Hayat Nasser 2, 3 Mita Nasipuri 1 Phuc Ngo 2, 3 Subhadip Basu 1 Isabelle Debled-Rennesson 2, 3
1 Jadavpur University, Kolkata
2 LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
3 ADAGIO - Applying Discrete Algorithms to Genomics and Imagery
LORIA - ALGO - Department of Algorithms, Computation, Image and Geometry
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.
Keywords : Hand Gesture Recognition Sign Language Kinect Statistical- Topological Features Depth Data Discrete Curve Polygonal Simplification
Type de document :
Communication dans un congrès
IWCIA, Jun 2017, Plovdiv, Bulgaria. 10256, LNCS. 〈10.1007/978-3-319-59108-7〉
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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. 10256, LNCS. 〈10.1007/978-3-319-59108-7〉. 〈hal-01517817〉

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