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International Conference on Theory and Applications of Fuzzy Systems and Soft Computing

ICAFS 2018: 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018 pp 129–136Cite as

Integrated Deep Learning Structures for Hand Gesture Recognition

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 896))

Abstract

In this paper, object control with hand movements is proposed for controlling distant objects even when the user is far from the system. This method is based on finding hands, predicting the states and direction of the hand movement. This human-computer interface (HCI) is an assistive system for users at near/far from objects. The model is specifically designed for controlling computer mouse on big screens during the formal presentation. By moving the hand to the left, right, up and down moves the mouse pointer and sends mouse button command using hand states. Here, close hand triggers mouse button, until the same hand is opened. In this system, Single Shot Multi Box Detection (SSD) architecture is used for object detection and Convolutional Neural Network (CNN) is used for predicting hand states. This integrated system allows users to control the mouse from a distant position without using any hardware. The test results show that this system is robust and accurate. This invention uses a single camera and aids users who are far from the computer during a presentation to shuffle through the slides.

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Author information

Authors and Affiliations

  1. Applied Artificial Intelligence Research Centre, Department of Computer Engineering, Near East University, Mersin-10, Nicosia, North Cyprus, Turkey

    Senol Korkmaz

Authors
  1. Senol Korkmaz
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Correspondence to Senol Korkmaz .

Editor information

Editors and Affiliations

  1. Joint MBA Program, Azerbaijan State Oil and Industry University, Baku, Azerbaijan

    Rafik A. Aliev

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  4. Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, USA

    Mo. Jamshidi

  5. Department of Mechatronics, Near East University, Nicosia TRNC, Turkey

    Fahreddin M. Sadikoglu

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Cite this paper

Korkmaz, S. (2019). Integrated Deep Learning Structures for Hand Gesture Recognition. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Sadikoglu, F. (eds) 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018. ICAFS 2018. Advances in Intelligent Systems and Computing, vol 896. Springer, Cham. https://doi.org/10.1007/978-3-030-04164-9_19

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