智慧型手勢辨識系統設計

Abstract

本論文主要目的是設計智慧型手勢辨識系統，此系統是根據人腦所認知手姿態狀態來識別不同的手勢，其中有九種手勢可以被此系統來描述，包含“向左”、“向右”、“左轉”、“右轉”、“向上”、“向下”、“熱機”、“追縱”和 “訓練”。手姿態的認知以及手勢的識別可以透過類神經網路的學習來處理，首先可利用觸發式類神經網路來達成手姿態的認知，再借由手勢分類器來完成手勢的識別。其中手勢分類器可分為前饋式類神經網路和遞迴式類神經網路兩種類型，雖然兩者都可以達到很好的手勢識別效能，但是仍以遞迴式的類神經網路為佳。

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.