自動化人臉情緒辨識系統一直是電腦視覺領域上的熱門研究，而這項技術也使的電腦更加的人性化。自動化人臉情緒辨識系統與許多領域息息相關，例如智慧生活以及醫學領域方面。在這篇論文中，我們使用人臉辨識常用的局部二元化圖樣來進行情緒辨識，並提出新的方法來改善結果。傳統的情緒辨識，會先利用Viola-Jones的方法，從影像中擷取出人臉。然而情緒辨識中，人的五官是比較重要的資訊，透過Viola-Jones擷取的人臉會有許多不必要的資訊如頭髮、耳朵以及背景。本文提出以主動形狀模型的方式來擷取人臉並保留重要的資訊。最後搭配支持向量機來分類開心、難過、厭惡、害怕、驚訝以及生氣這六種表情。

The automatic emotion recognition system has been a popular issue in computer vision area. With emotion recognition system, computer becomes more humanized. It also brings strong impacts on many areas such as smart living and medical area. In this thesis, I use the LBP method, which was commonly used in facial expression recognition. Furthermore, We propose a novel idea to improve the result. In traditional facial expression recognition, the researchers use Viola-Jones method to crop face from input image. However, the cropped face contains unimportant information such as hair, ear and background. Thus, ASM method was used to adjust the cropped face and keep important information. Finally, we distinguish six expressions as happiness, sadness, disgust, fear and surprise with SVM.

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