Static Hand Gesture Recognition Based on Fusion of Moments

  • Subhamoy Chatterjee
  • Dipak Kumar Ghosh
  • Samit Ari
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 309)


A vision-based static hand gesture recognition algorithm which consists of three stages: pre-processing, feature extraction and classification are presented in this work. The pre-processing stage comprises of following three sub-stages: segmentation, which segments hand region from its background using YCbCr skin colour-based segmentation process; rotation, that rotates segmented gesture to make the algorithm, rotation invariant; Morphological filtering, that removes background and object noise. Non-orthogonal moments like geometric moments and orthogonal moments like Tchebichef and Krawtchouk moments are used here as features. To improve the performance of classification, two feature fusion strategies are proposed in this work: serial feature fusion and parallel feature fusion. A feed-forward multi-layer perceptron (MLP)-based artificial neural network classifier is proposed. A user-independent experiment is conducted on 1,500 gestures of 10 classes for 10 different users.


American sign language digits Geometric moment Tchebichef moment Krawtchouk moment Serial feature fusion Parallel feature fusion Artificial neural network 


  1. 1.
    Gupta, S., Jaafar, J., Ahmad, W.F.W.: Static hand gesture recognition using local gabor filter. In: International Symposium on Robotics and Intelligent Sensors (IRIS 2012), vol. 41, pp. 827–832, Kuching, Sarawak, Malaysia (2012)Google Scholar
  2. 2.
    Priyal, S.P., Bora, P.K.: A robust static hand gesture recognition system using geometry based normalization and krawtchouk moments. Pattern Recogn. 46(8), 2202–2219 (2013)CrossRefGoogle Scholar
  3. 3.
    Kumar, P., Verma, J., Prasad, S.: Hand data glove: A wearable real-time device for human-computer interaction. Int. J. Adv. sci. Technol. 43 (2012)Google Scholar
  4. 4.
    Huang, Y., Monekosso, D., Wang, H., Augusto, J.C.: A concept grounding approach for glove-based gesture recognition. In: 7th International conference On Intelligent Environments (IE), pp. 358–361, Nottingham, UK (2011)Google Scholar
  5. 5.
    Yang, J., Yang, J.Y., Jhang, D., Lu, J.F.: Feature fusion: Parallel strategy versus serial strategy. Pattern Recogn. 36(6), 1369–1381 (2003)CrossRefMATHGoogle Scholar
  6. 6.
    Aibinu, A.M., Shafie, A.A., Salami, M.J.E.: Performance analysis of ANN based YCbCr Skin Color Detection Algorithm. In: International Symposium on Robotics and Intelligent Sensors (IRIS), vol. 41, pp. 1183–1189, Sarawak, Malaysia (2012)Google Scholar
  7. 7.
    Ghosh, D.K., Ari, S.: A static hand gesture recognition algorithm using K-Mean based radial basis function neural network. In: 8th International Conference on Information, Communications and Signal Processing (ICICS), pp. 1–5, Singapore (2011)Google Scholar
  8. 8.
    Priyal, S.P., Bora, P.K.: A study on static hand gesture recognition using moments. In: International Conference on Signal Processing and Communications (SPCOM), pp. 1–5, IISC, Bangalore (2010)Google Scholar
  9. 9.
    Zou, Z., Premaratne, P., Monaragala, R., Bandara, N Premaratne, M.: Dynamic hand gesture recognition system using moment invariants. In: 5th International Conference on Information Automation for Sustainability (ICIAFs), pp. 108–113, Colombo, Sri Lanka (2010)Google Scholar
  10. 10.
    Haykin, S.: Neural networks. Prentice-Hall (1999, 2nd edn.)Google Scholar
  11. 11.
    Daamouche, A., Hamami, L., Alajlan, N., Melgani, F.: A wavelet optimization approach for ECG signal classification. Biomed. Signal Process. Control 7(4), 342–349 (2012)CrossRefGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Subhamoy Chatterjee
    • 1
  • Dipak Kumar Ghosh
    • 1
  • Samit Ari
    • 1
  1. 1.Department of Electronics and Communication EngineeringNational Institute of TechnologyRourkelaIndia

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