幸福科技的目標是將沒感到幸福的人變成感到幸福的人，本論文提出以皺眉當作負面情緒，而微笑當作正面情緒，結合正負面表情觀測，以達到幸福科技的觀測目標。本論文提出一個新的表情辨識單位，臉部表情單位(FEU)。此單位可將表情的辨識系統模組化。臉部表情向量可將表情空間作更細微的切割，有助於人機介面(HMI)中，機器瞭解人類表情的提升。此表情辨識系統以索貝爾(Sobel)濾波器作皺眉特徵值擷取，以兩個閥值作皺眉定義。辨識笑臉的部分，使用了新的特徵值擷取演算法-嘴角特徵值(MCF)。本實驗因為是模組化的，可將兩個辨識器的結果完全獨立。笑臉部分的辨識率，在現場測試(real-time test)結果正確率高於80%。且在資料庫中測試正確率高達88%以上。在辨識皺眉的部分，在現場測試(real-time test)結果正確率亦高於80%。資料庫測試中，辨識結果高達85%以上。模組化的結果，更可降低重新訓練(re-train)的時間消耗(cost)。實驗結果顯示本研究可有效偵測與辨識微笑及皺眉。

Orange Technology aims to offer some technological support to a certain extend to make people happy. Indeed, to understand the status of happiness of human beings, detection of negative and positive expressions plays a crucial role which could benefit to develop the system more reliable. In this purpose, this work focuses to develop an expression recognition system which considers frown and smile as negative and positive expression, respectively. The Facial Expression Unit (FEU) is proposed as the basic unit in facial-space, the FEU is also modularized as an expression recognition system. FEUs could always represent the facial-space with more classifications and this information is helpful to recognized expression from machine’s perspective in Human Machine Interface (HMI). The proposed Mouth Corner Feature (MCF) retrieves the smile and Sobel filter is adopted for frown detection. The experimental results can be observed independently, in smile detection, the real-time test accuracy rate is greater than 80% and 87% for off-line (database) test. As well as for the frown detection, 80% accuracy is achieved for real-time test and 85% for off-line test. The modularize system can further reduce the time consumption when re-training is required. As a result, the proposed system is efficient in both of smile and frown detection.

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