本文旨在實現人臉偵測、辨識人臉姿態與追蹤於即時動態影像，透過攝影機擷取影像，首先利用閥值把膚色與影像分離，經過形態學處理，把不必要的雜訊給移除，最後使用種子區域生長法，標記每個膚色的區塊，使用人臉判定法，假如膚色區塊不是人臉的話就被捨去掉。 當人臉被偵測出來，使用小波圖像分割法，抓取低頻子影像去做辨識，使用二維主成份分析(2DPCA)演算法去做辨識，辨識結果為側臉就不進行追蹤，只有辨識為正臉才進行追蹤。 當正臉被辨識成功，找出中心點的位置，對人臉建構顏色直方圖模型，人臉追蹤演算法是使用自組織隨時調變係數的粒子最佳化 (HPSO-TVAC) 演算法，當人臉遇到遮蔽問題，本文使用自適應搜尋框，當找不到人臉時使用較大的搜尋框，搜尋框的縮放是依照群體最佳適應值。 ;The main purposes of this thesis are to achieve human face detection and head posture recognition, as well as to track a dynamic image in real time via camera. First, skin-color region is detected, after morphological operations, unnecessary noise is removed, and the method of seed region growing is used to mark pixel blocks. Then the skin-color region is determined whether or not each block is a human face. If it is not human face, it is discarded. Otherwise, wavelet transform is used to decompose the face image. A low-frequency sub-band face image is captured by wavelet transform, and two-dimensional principle component analysis (2DPCA) is used to recognize head posture. Face color histograms are used to build face models, and faces are traced by the Self-Organizing Hierarchical Particle Swarm Optimizer With Time-Varying Acceleration Coefficients (HPSO-TVAC) algorithm. In order to solve the face masking problem, adaptive seeking windows are applied. When a human face is not detected, a large seeking window will be used, which will zoom in or out depending on the best global fitness.
