本篇論文旨在發展一套自動化之表情辨識系統，由影像輸入後即可自動化的偵測人臉、擷取人臉特徵，並進行表情辨識。
人臉偵測部分我們以Adaboost訓練學習演算法，挑選適當之Haar-Like矩形特徵，結合各個弱分類器而組成一個強健分類器來偵測人臉。
特徵擷取部分我們將人臉分割成各個不同的區域，並對每個區域採用不同之擷取方法，主要是利用灰階值的差異、大小與形狀的不同、以及顏色之相異等想法，配合不同的影像處理技術來凸顯以及定位特徵點。
表情辨識的部分則使用近年來相當熱門的機器學習系統：SVM分類器，來做為表情分類之依據。我們以所擷取之特徵點轉換成一個15維的表情特徵向量，輸入到SVM分類器做表情辨識。實驗包含有SVM核函數與其參數之選取，實驗結果顯示本系統之表情辨識系統有不錯之辨識率。

This thesis aims to develop a system of recognizing facial expressions automatically. After inputting the image, the system will automatically detect the faces, extract the features of faces and proceed to identify the facial expressions.
In the section of face detection, Adaboost algorithm will be used for selecting the appropriate Haar-like features, and combine all weak classifier to form a strong classifier for recognizing the faces.
As to the feature of extraction, the faces will be divided up into different areas and they will be extracted by various acquisition methods. Our scheme uses the dispersion of the grey-level, and differences in proportions, shapes, and colours with associated image processing methods for showing and labelling the feature points.
The system of recognizing facial expressions is based on the most popular SVM classifier. The extracted feature points will be transformed into 15-dimensional facial feature vectors and input into the SVM classifier for facial expression recognition. Experimental result shows the good performance on the rate of the facial expression recognition system.

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