Real-Time Hand Tracking and Gesture Recognition System Using Neural Networks
Authors: Tin Hninn Hninn Maung
This paper introduces a hand gesture recognition system to recognize real time gesture in unstrained environments. Efforts should be made to adapt computers to our natural means of communication: Speech and body language. A simple and fast algorithm using orientation histograms will be developed. It will recognize a subset of MAL static hand gestures. A pattern recognition system will be using a transforrn that converts an image into a feature vector, which will be compared with the feature vectors of a training set of gestures. The final system will be Perceptron implementation in MATLAB. This paper includes experiments of 33 hand postures and discusses the results. Experiments shows that the system can achieve a 90% recognition average rate and is suitable for real time applications.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1333642Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4270
 Journal of WSCG, Vol.12, NO 1-3, ISSN 1213-6972
 V.I. Pavlovic, R. Sharma, T.S. Huang. Visual interpretation of hand gestures for human-computer interaction, A Review, IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7): 677-695, 1997.
 J.Davis, M.Shah. Recognizing hand gestures. In Proceedings of European Conference on Computer Vision, ECCV: 331-340, 1994.
 D.J.Turman, D. Zeltzer. Survey of glove-based input. IEEE Computer Graphics and Application 14:30-39, 1994
 Starner, T. and Pentland. Real-Time American Sign Language Recognition from Video Using Hidden Markov Models, TR-375, MIT Media Lab, 1995.
 R.Kjeldsen, J.Kender. Visual hand gesture recognition for window system control, in IWAFGR: 184-188, 1995.
 M.Zhao, F.K.H. Quek, Xindong Wu. Recursive induction learning in hand gesture recognition, IEEE Trans. Pattern Anal. Mach. Intell. 20 (11): 1174.
 Hyeon-Kyu Lee, Jin H. Kim. HMM_based threshold model approach for gesture recognition, IEEE Trans. Pattern Anal. Mach. Intell.201(10):961-973,1999.
 Ho-Sub Yoon,Jung Soh,Younglae J. Bae, Hyun Seng Yang. Hand Gesture recognition using combined features of location, angle, velocity, Pattern Recognition 34:1491-1501,2001.
 R.Locken, A.W. Fitzgibbon. Real gesture recognition using deterministic boosting , Proceeding of British Machine Vision Conference, 2002.
 Hand Gesture Recognition Using Neural Networks by Klimis Symeonidis.
 Duane Hanselman, Bruse Littlefield, Mastering MatLab, A comprehensive tutorial and reference, Prentice Hall.