Hand Gestures Categorisation and Recognition
In this digital era, the focus is now on the development of applications that allow human beings and machines to interact directly. Up to now, many hand gesture recognition systems have been developed for different applications such as sign language recognition and smart surveillance. In recent years, researchers have shown interest in the development of hand gesture recognition applications for dancing movements, which involve dynamic hand gestures. However, there are still various challenges such as extraction of invariant factors, automatic segmentation, the transition between gestures, mixed gestures issues, nature of dynamic hand gestures and occlusions that need to be addressed. This research work aims at developing an application to categorise and recognise the classical “Bharatanatyam” dance hand gestures. Since no online database of “Bharatanatyam” gestures is available to the public for research purposes, a customised database has been built with 900 images, consisting of 15 instances for each hand gesture. In this work, Chain Codes and Histogram of Oriented Gradients (HOG) are proposed for the feature representation of the hand gestures. For the classification of the images, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) are explored. From the experiments conducted, Chain codes with SVM provide a recognition rate of 99.9% and a false rejection rate of only 0.1%, which is a promising technique for the deployment of movement recognition applications.
KeywordsHand gestures Chain codes HOG SVM KNN
We have taken the required permission of dataset, images used in this work from the respective authorities. We are solely responsible if any problem that arises in the future.
- 2.Malima, A.K., Özgür, E., Çetin, M.: A fast algorithm for vision-based hand gesture recognition for robot control. In: IEEE 14th Signal Processing and Communications Applications, Antalya (2006)Google Scholar
- 3.Saha, S., Konar, A., Gupta, D., Ray, A., Sarkar, A., Chatterjee, P., Janarthanan, R.: Bharatanatyam hand gesture recognition using polygon representation. In: Control, Instrumentation, Energy and Communication (CIEC), 2014 International Conference on, IEEE, pp. 563–567 (2014)Google Scholar
- 4.Panchal, J.B., Kandoriya, K.P.: Hand gesture recognition using clustering based technique. Int. J. Sci. Res. (Ijsr). 4(6), 1427–1430 (2015)Google Scholar
- 6.Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. Cengage Learning, 4th edn (2014)Google Scholar
- 7.Dongare, Y.B., Patole, R.: Skin color detection and background subtraction fusion for hand gesture segmentation. Int. J. Eng. Res. Gen. Sci. 3(4) (2015)Google Scholar
- 8.Hearn, D., Baker, M.P., Baker, M.P.: Computer Graphics with OpenGL, vol. 3. Pearson Prentice Hall, Upper Saddle River, NJ (2004)Google Scholar
- 9.Sarkar, A.R., Sanyal, G., Majumder, S.: Hand gesture recognition systems: a survey. Int. J. Comput. Appl. 71(15) (2013)Google Scholar
- 10.Soille, P.: Erosion and dilation. In Morphological Image Analysis (pp. 63–103). Springer, Berlin, Heidelberg (2004)Google Scholar
- 11.Andrew, M.: Introduction to Digital Image Processing with Matlab, USA: Thomson Course Technology, pp. 353 (2004)Google Scholar
- 12.Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893 (2005)Google Scholar
- 13.Zou, Z., Premaratne, P., Monaragala, R., Bandara, N., Premaratne, M.: Dynamic hand gesture recognition system using moment invariants. In: IEEE 5th International Conference on Information and Automation for Sustainability (ICIAFs), pp. 108–113 (2010)Google Scholar