Clustering techniques for human posture recognition: K-means, FCM and SOM
SSIP '09/MIV'09 Proceedings of the 9th WSEAS international conference on signal, speech and image processing, and 9th WSEAS international conference on Multimedia, internet & video technologies
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This paper describes a Gesture Recognition system in which a hand gesture commands are Recognized. A Fuzzy C-Means clustering method is used to classify hand postures as "gestures". The fuzzy recognition system was tested for both User Dependent and User Independent Gestures vocabulary. Results revealed recognition rate (the ratio of User Independent gestures to User Dependent gestures) recognition accuracy the percent of he User Dependent gestures recognized correctly of 100%. And User Independent Gestures Recognized Correctly of 54%. No gestures was recognized incorrectly. Performance times to carry out the pushing task showed rapid learning, reaching standard times within 4 to 6 trials by an inexperienced operator.