Intelligent Hand Gesture Recognition System Design
|關鍵字:||手勢辨識;類神經網路;手姿態;Gesture recognition;Neural Network;Hand posture|
The main purpose of this thesis is to design the intelligent hand gesture recognition system, which can recognize different hand gestures according to cognitive posture states of human brain. There are nine hand gestures which can be described by this system, including “Turn right”, “Turn left”, “Upward”, “Downward”, “Right around”, “Left around”, “Warming”, “Following” and “Learning”. The cognition of hand posture states and recognition of hand gestures can be learned by neural network. A hand gesture analyzer, composed of a repeated state retriever and a gesture classifier, is applied to recognize the hand gestures. The hand gesture is closely related to the change of hand posture states; therefore, a repeated state retriever is used to turn hand posture state sequence into triggered state sequence, which can be further classified by the gesture classifier. The gesture classifier can be implemented by two types of neural network, feed-forward and recurrent. It can be shown that both types of gesture classifier can well recognize the hand gestures. However, since the feed-forward classifier is often interfered by undefined hand posture state sequence, the recurrent classifier has a better result in had gesture recognition.
|Appears in Collections:||Thesis|
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