在這篇論文中，我們提出了一個利用光流場及倍率影像之監督式LLE分析的臉部表情辨識演算法。在這個演算法中，我們首先對臉部影像擷取出正確的臉部區域，藉此去除整體頭部運動所造成的影響。在第二部分，我們在沒有表情跟有表情的影像之間計算出光流場及倍率影像，然後應用SLLE從表情運動及亮度變化擷取出低維度具有代表性的特徵。第三部分，我們計算在低維度特徵向量的距離來辨識臉部的表情。最後我們結合光流場及倍率影像來改善臉部表情分類。在JAFFE實驗的結果顯示，我們提出的方法比之前的方法在臉部表情辨識上較為傑出，我們也用Yale Face資料庫來測試這個藉由JAFFE訓練出來的表情辨識系統，在表情辨識率上仍然有相當好的效果。所以，我們成功地利用不同的資料庫來作測試並且這個辨識結果比得上在JAFFE測試的時候。實驗結果顯示，這個系統不僅能夠在JAFFE資料庫有不錯的結果，在Yale Face資料庫也能夠有相當好的效果。

In this thesis, we propose a new facial expression recognition algorithm based on supervised locally linear embedding (SLLE) analysis on the optical flow and ratio image. In this algorithm, we first extract the face region from the face image to remove factors due to global head motion. Secondly, we compute the optical flow and ratio image between the neutral face and expression images and then apply the SLLE to extract the low-dimensional discriminating features from the expression motion and brightness variation. Thirdly, we compute the distance between the low-dimensional feature vectors to recognize the facial expression. Finally, we combine optical flow and ratio image properly to improve the facial expression classification. The experimental results on the JAFFE face database show the proposed algorithm outperforms the previous methods for facial expression recognition. We also use the Yale Face database for testing the expression recognition system trained from the JAFFE database. It still has good performance on the expression recognition rate. Therefore, we successfully use different database for testing and the result is comparable with testing on the same JAFFE database. The result shows that the system not only works well on JAFFE database but also has good performance on the Yale Face database.

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