Hand Gesture Identification and Recognition using Modern Deep Learning Algorithms
M Lakshmi1, G Sree Sahithi2, J Lakshmi Pravallika3, Kolla Bhanu Prakash4
1M Lakshmi*, Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India.
2G Sree Sahithi, Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India.
3J Lakshmi Pravallika, Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India.
4Kolla Bhanu Prakash, Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP:5027-5031 | Volume-9 Issue-1, October 2019 | Retrieval Number: A3004109119/2019©BEIESP | DOI: 10.35940/ijeat.A3004.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper, we proposed an approach towards the real-time hand gesture recognition using the Gaussian Mixture-based Background / Foreground Segmentation Algorithm. We proposed a method for feature extraction by using measurements on joints of the extracted skeletons. The proposed algorithm will build a background subtract model to get the foreground image. We applied Gaussian blur to the foreground image and threshold for binary images. The contour hull and convexity are used to build a 3D image of the hand gesture recognition. We constructed a dataset and defined the gestures. We trained them by gesture classifiers by some assumptions such that those can be easily understood. Experimental results proved the effectiveness and potential of our modern deep learning approach.
Keywords: Hand gesture recognition, Gaussian Mixture, Deep learning, Clustering, Segmentation, Threshold.