本研究期望建立一套規則式邏輯作為手勢辨識之基礎，以一台攝影機擷取手勢影像來達成即時手勢辨識的目的。研究方法是透過一些自然手勢的表達來找出膚色區塊及距離的相關特徵值，選定的手勢是挑選10個數字手勢。受試者共有13人，分為訓練組(3人)及測試組(10人)，由訓練組建立手勢資料庫，再藉由測試組的資料進行驗證。實驗設計是藉由一台攝影機抓取受試者手部膚色影像，手勢影像經由影像處理程序後，取得手指平面座標經過特徵值擷取，藉由觀察法找出手勢分類的重要特徵，以發展手勢辨識系統(Finger Gesture Recognition System, FGRS)，進而完成手勢之辨識。
由於系統僅由一台攝影機取像，手勢影像容易受到遮蔽效應的影響，因此實驗過程中有取像條件限制。實驗之結果說明系統的平均整體辨識率都在九成以上，僅手勢六不到九成，只有八成四；個別辨識率除了手勢六與手勢八之外也都超過八成以上。經由討論分析後將錯誤辨識結果區分為兩類，合理錯誤(佔52%)與不合理錯誤(佔48%)，合理的錯誤是由於判別特徵值落在臨界值邊緣之模糊地帶，不合理的錯誤主要時受到取像環境的影響。因此本研究認為在影像處理程序中加入手指個別標記等程序，應能降低誤判率，使整體的手勢辨識績效達到最佳。

In this study, we hope to establish a rule-based logical hand gesture recognition system, which used one camera to catch hand gestures for the purpose of recognition. We use the research method of using natural gestures, which are ten gestures of numbers from zero to nine, to find out the relative characteristic value between blocks of skin color and distances. There are thirteen subjects, three of them are used for training and other ten are used for gesture recognition. The hand gesture database is developed by the training group and is tested and verified by the other 10 subjects. This experiment is designed to catch the image of the subjects’ skin color by one camera. After we process the gesture images, we will get the 2D coordinate of fingers. And through the observation method we will find out the important characteristic of gesture classification, further we can develop the finger gesture recognition system (FGRS).
The images are easily influenced by masking effect because we only used one camera to catch the hand gestures. So we set catching image limits in the experiment process. The result of the experiment show that all of the average total recognition rates are above 90%. Except the gesture six didn’t get to 90%, it only got to 84%. The individual recognition rates all are more than 80% besides gesture six and gesture eight. After analyzing and discussing we divide the mistake results into two types, reasonable mistakes (52%) and unreasonable mistakes (48%). Reasonable mistakes is due to the value of attributes lie in indistinct region of threshold limit value, and unreasonable mistakes are mainly influenced by the environments of catching image. Therefore this study thinks that it can decrease the mistaken judging rates by marking each finger during image processing, and it can make the recognizing result reaches optimum

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