Performance Improvement of Hand Gesture Recognition By using Sparse Coding With Kinect V2 Sensors
S. Chandra Sekhar1, N.N. Mhala2

1S.Chandra Sekhar, *Ph.D. Scholar, Bapurao Deshmukh College of Engineering, Sevagram Wardha, Maharashtra State-442102, India 
2Dr. N.N. Mhala, Professor& Principal, Government Polytechnic College. Thane, Maharashtra State, India.
Manuscript received on 24 August 2019. | Revised Manuscript received on 04 September 2019. | Manuscript published on 30 September 2019. | PP: 3947-3950 | Volume-8 Issue-11, September 2019. | Retrieval Number: K19760981119/2019©BEIESP | DOI: 10.35940/ijitee.K1976.0981119
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Abstract: This paper presents an important technique with improvement property for image identification intention. Here the important technique which is sparse coding representation acts as a major ROLE in achiveving One-short learning and real recognition of actions[1]. The implementation method based on mainly 3-D Histogram of prospect flow with Global Histograms of orientated Gradient .The major and most important of this method is to use imprison on major level regions from the given data sets . With this data then suggest a instantaneous to get video segmentation and video gratitude of hand gesture action by using linear SVMs.This paper mainly highlights the major role of sparse coding technique to stand for 3D proceedings [2] .From this paper we obtain very good results in an domestic dataset captured by Kinect V2 sensors together with hand gesture proceedings and complex hand gesture actions differing by small details.
Keywords: Adaptive Sparse Coding. Real-Time hand gesture. One-Shot learning. Kinect V2 Sensors. INTRODUCTION
Scope of the Article: Network Performance; Protocols; Sensors