本論文提出一個適用於即時性(real-time)互動環境中，以數位影像處理技術開發主動式多解析度人臉辨識系統，以自動化及非接觸方式開發人機介面。人臉特徵抽取運用灰度不變特性之局部二元樣板(local binary pattern, LBP)紋理描述，搭配局部方向樣板(local directional pattern, LDP)紋理描述降低誤判率。為了提高表情造成的局部特徵變異容忍程度，故本論文提出非線性人臉影像區域切割統計紋理特徵直方圖演算法，依據平均樣板變異數矩陣及加速強健性特徵(speeded up robust features, SURF)角點匹配驗證人臉局部具鑑別性及抗變異性區域劃分為九格非線性子區塊，個別統計紋理直方圖以串接方式將影像局部與整體關係互相聯結並搭配權重式卡方匹配檢定，經實驗證實本論文提出非線性人臉影像區域切割統計紋理特徵演算法能有效抵抗表情變化並提昇人臉辨識準確率、降低誤判率，也可適用於大眾環境之性別辦識。針對即時主動的人機介面應用環境，除了表情變化問題，人機距離使影像具有不同解析度之情況，將嚴重影響到辨識穩定度。本論文提出三維紋理鑑別特徵方法，結合時間、空間資訊及多解析度人臉影像紋理分析並線性累加成整合直方圖，運用正規化突顯個人面部影像於不同時間、空間及距離所共存之主要紋理分佈。因此使用者可於不同時間、空間及距離皆可人機互動，系統依然能準確地辨識使用者身份，並且搭配自動化鑑別特徵學習機制及其權重更新，於運作時間不斷推移所致各種變異皆保持穩定地辨識效果。

We proposed to perform face recognition in an active and multi-resolution approach. The design target is to provide a real-time natural (non-contact) human interface for an IPTV system. For face image features, both local binary pattern and local directional pattern are adopted for robustness. In addition, these features and histogram statistics are extracted from one face image which is decomposed into nine blocks with different size. These decomposed face block regions are determined from an average face from which fast robust features for recognition can be obtained. The histogram statistics are extracted from each region individually and then are concatenated to yield the final feature vector. Weighted chi-square measurement is utilized for face recognition. Experiments verified that the proposed active face recognition method is insensitive to changing facial expressions. In addition, higher recognition accuracy and lower false positive rate can be achieved, which can also be applied to gender recognition. To develop an active human-machine interface under the condition that the face recognition has to be carried out in a multi-resolution approach, we proposed to use three-dimensional (3D) histogram which comprised histogram statistics across both time and space dimensions. The most distinguished feature of the proposed method is that it can perform face recognition very well when peoples are with different distances to the camera. In addition, the weighting factors are subjected to be updated in the recognition process and the discriminated features are selected through an learning algorithm, such that the system can maintain a stable recognition accuracy.

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