新生兒很難使用言語來表達自己的需求，此時能透過聲音、肢體動作以及臉部表情來獲得嬰兒的需求。本論文希望能發展一套輔助系統，藉由監控嬰兒的臉部表情可以防止意外的發生以及得知嬰兒覺得不舒服的情況。
本研究針對1至5個月大的嬰兒進行監控，將攝影機置於嬰兒臉部正上方，使得嬰兒臉部置於影像當中，並且影像區域能涵蓋整個嬰兒臉部。本系統分成兩個部分，分別為嬰兒臉部偵測以及嬰兒臉部表情分類。在嬰兒臉部偵測方面，使用膚色特徵做為嬰兒臉部偵測的依據，利用k-means clustering演算法的特性，將影像中顏色特徵相近的像素點群聚在一起。在嬰兒臉部表情分類方面，使用Tchebichef moments當作特徵值，將臉部分成多個區塊，分別計算各區塊Tchebichef moments值，之後輸入至自組型類神經網路(self-organizing map neural network) 獲得臉部表情分類的結果。

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