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Fourier Descriptors Based Hand Gesture Recognition Using Neural Networks

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Innovative Data Communication Technologies and Application (ICIDCA 2019)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 46))

Abstract

With the advanced computing and efficient memory utilization, vision-based learning models are being developed on a large scale. Sign language recognition is one such application where the use of Artificial Neural Networks (ANN) is being explored. In this article, the use of Fourier Descriptors for hand gesture recognition is discussed. The system model was set up as follows: Images of 24 hand gestures of American Sign Language (ASL) were subjected to edge detection algorithms, the contours were extracted and Complex Fourier descriptors were obtained as features for classification using a 2 layer feed-forward neural network The effects of subsampling of the contour, number of hidden neurons and training functions on the performance of the network were observed. Maximum accuracy of 87.5% was achieved.

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Authors and Affiliations

  1. EXTC, Thadomal Shahani Engineering College, Mumbai University, Mumbai, India

    Rajas Nene, Pranay Narain, M. Mani Roja & Medha Somalwar

Authors
  1. Rajas Nene
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  2. Pranay Narain
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  3. M. Mani Roja
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  4. Medha Somalwar
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Corresponding author

Correspondence to Rajas Nene .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Gnanamani College of Technology, Namakkal, India

    Jennifer S. Raj

  2. College of Computer Engineering and Sciences, Prince Mohammad Bin Fahd University, Al-Khobar, Saudi Arabia

    Abul Bashar

  3. Purdue Polytechnic Institute, Purdue University, West Lafayette, IN, USA

    S. R. Jino Ramson

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Nene, R., Narain, P., Roja, M.M., Somalwar, M. (2020). Fourier Descriptors Based Hand Gesture Recognition Using Neural Networks. In: Raj, J., Bashar, A., Ramson, S. (eds) Innovative Data Communication Technologies and Application. ICIDCA 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 46. Springer, Cham. https://doi.org/10.1007/978-3-030-38040-3_16

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