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Static Hand Gesture Recognition using Convolutional Neural Network with Data Augmentation
Department of Computer Science and Engineering University of Chittagong, Bangladesh.
University of Chittagong, Bangladesh.ORCID iD: 0000-0002-7473-8185
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0002-3090-7645
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-0244-3561
2019 (English)In: Proceedings of the Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), IEEE, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Computer is a part and parcel in our day to day life and used in various fields. The interaction of human and computer is accomplished by traditional input devices like mouse, keyboard etc. Hand gestures can be a useful medium of human-computer interaction and can make the interaction easier. Gestures vary in orientation and shape from person to person. So, non-linearity exists in this problem. Recent research has proved the supremacy of Convolutional Neural Network (CNN) for image representation and classification. Since, CNN can learn complex and non-linear relationships among images, in this paper, a static hand gesture recognition method using CNN was proposed. Data augmentation like re-scaling, zooming, shearing, rotation, width and height shifting was applied to the dataset. The model was trained on 8000 images and tested on 1600 images which were divided into 10 classes. The model with augmented data achieved accuracy 97.12% which is nearly 4% higher than the model without augmentation (92.87%).

Place, publisher, year, edition, pages
IEEE, 2019.
Keywords [en]
Convolutional Neural Network, Static hand gestures recognition, Data augmentation.
National Category
Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-73308OAI: oai:DiVA.org:ltu-73308DiVA, id: diva2:1299000
Conference
Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), 26 - 29 April 2019, Spokane, United States
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Funder
Swedish Research Council, 2014-4251Available from: 2019-03-25 Created: 2019-03-25 Last updated: 2019-04-02Bibliographically approved

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Islam, Raihan UlAndersson, Karl

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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Language
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Output format
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