在本論文中，我們利用空間領域下人臉前景與景物背景能量差異之特性及頭部輪廓特徵，提出了一個新穎、有效且快速的頭部輪廓偵測法 (Head Corner Detection, HCD)。於BioID、VISIONICS及DCL (自行拍攝) 的三種資料庫測試結果，人臉定位正確率分別為97.96%、92.92%及93.59%，基於影像大小差異，平均定位偵測需時小於2秒。由實驗結果得知，本方法確實可以有效的克服未經控制的環境所造成的偵測困難度，以應用於複雜背景下之單一人臉偵測定位。

This paper proposes a novel two-step approach for face detection in complex scenes. As image capturing is strictly related to reflection of lighting, it has been observed that a face in the foreground is more vulnerable to lightness than environment in the background. The framework of the proposed method resolves mainly around the gap based on energy variation between the face and the scene. To speed up the process, this approach adopts energy analysis to face localization strategy. We present energy image analysis to remove most noise-like regions so as to enhance face localization performance, and then devise the head corner detection (HCD) approach to search for the best combinations of facial sides and head corners with anthropometric measures, and thereafter the facial interest region is located. The experimental results demonstrate that the performance of our proposed method has significant improvement on three head-and-shoulder databases, when compared with related work.