基於曲線擬合及支持向量機之人臉微笑辨識系統

Smile Detection Based on Curve Fitting and Support Vector Machine

本研究提出一套使用兩個色彩空間特徵之支持向量機(Support Vector Machine)來偵測人臉並辨識人臉上的微笑程度。首先將人臉偵測分為兩大部分，分別為膚色擷取與人臉偵測。膚色擷取使用YCbCr及NCC (Normalized Color Coordinates)色彩空間，以降低複雜光線對膚色取樣影響程度。透過兩色彩空間轉換後得到YCbCr膚色及NCC膚色區域，再使用標準聯集(Standard Union)將膚色區域從影像中切割出來。最後將找出的膚色區域輸入到已訓練的支持向量機，判斷該區域是否為人臉，應用本系統於人臉偵測可達94.73%的正確率。人臉定位完成後，透過邊緣濾波器定位雙眼座標並依據雙眼座標及嘴巴與人臉相對應的幾何空間找出嘴巴區域，對嘴唇邊緣做曲線擬合找最大曲率代表微笑程度，以期望使用者與系統之間能夠有更直覺性的互動，例如讓機器人能了解人類的情緒反應，或者可以應用於醫學領域協助醫生評估患者的情緒狀態，亦或者用在視訊通話手機中用以偵測講話者之情緒，也可用於服務業時時提醒員工保持完美笑容來吸引更多顧客。

The main purpose of this study is to present a method that applies Support Vector Machines maintained by two color spaces to Face Detection. First of all, the researcher transferred an image from RGB color space to YCbCr and Normalized Color Coordinates (NCC) color space to reduce the influence affected by different light condition to skin sample. After color space translation, the researcher could obtain YCbCr skin and NCC skin, and then found out some skin regions from original image by fuzzy computation. Finally, these skin regions were inputted into Support Vector Machines to distinguish which are or are not human faces. The result of this research appears that the overall accuracy rate of this system is 94.73%. After face detection, which is to identify the position of the mouth according to the eye coordinates, the researcher can use curve fitting to calculate the degree of a smile. It is hoped that by applying the result of this research to many situations more intuitive interactions between users and the systems can be obtained. For example, robots with this kind of equipment can understand the emotional reactions of humans. This technique could also be used to assist doctors to estimate the emotions of their patients, or to help people understand the emotions of their counter parties during cell phone communications. It can serve as a reminder for employees in the service business to always maintain smiles to attract more customers.