本論文提出一個適用於即時性(real-time)互動環境，以數位影像處理技術開發主動式多景深人臉辨識系統，以自然式及非接觸方式開發人機介面，不僅能有效識別資料庫內已註冊之使用者，並且能判斷不存在資料庫內的未註冊者。本系統以居家收視環境模擬，為了克服因收視環境背景過於複雜可能造成人臉影像的誤偵測，收視距離、時間及位置不一可能造成人臉辨識上的誤判，本系統利用Microsoft kinect sensor所提供的景深資訊及人型骨架(skeleton)資訊。當收視環境存在收視者時，可利用人型骨架的頭部關節部位快速擷取人臉影像可能所在的區域，經由人臉偵測演算法判斷是否為真，再將偵測後的人臉影像利用人臉影像遮罩去除可能因環境複雜所產生的雜訊，並依據景深資訊判斷收視者距離後選擇對應的資料庫來辨識人臉。人臉特徵擷取是運用灰度不變特性之多區塊局部二元樣板(multi-scale block local binary pattern, MBLBP)紋理描述，並將人臉影像以1×1、2×2及3×3階層式(hierarchical)拆解成14個子區塊後擷取紋理特徵，並個別統計成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卡方匹配檢定及積分系統辨識身分。此外，並搭配自適應更新機制，透過學習方式克服收視者可能會在不同時間及位置收視，因光源而產生不同的光影變化，因此設計自適應更新學習方式以增加資料庫內人臉影像紋理直方圖的強健性。

An active face recognition system (AFR) is proposed to act as the real-time human-machine interface for an interactive TV (iTV) system. This AFR system is developed to recognize multi-persons with multi-depth of field for the iTV to recommend interested TV programs for current group users. It is assumed to operate in the family environment with four to six members and designed to recognize registered users and reject (identify) unregistered ones for the program recommender. To improve the 2D face image based recognition method, the 3D scene depth information is also acquired to eliminate false positive recognition. The Microsoft Kinect Sensor is utilized to provide the scene depth and body skeleton information to enhance the processing time and stability of the AFR. With the help of body skeleton, the face image area can be quickly located for the AFR to justify faces within which efficiently. The detected face is first filtered by an average face alpha mask for noise free and its scene depth is used to select corresponding face models from databases for recognition. For robust face feature description, we proposed to adopt multi-scale block local binary pattern, MBLBP. One face image is hierarchically decomposed into 1×1, 2×2, and 3×3 sub-blocks, 14 sub-blocks in total, from which the MBLBP features are extracted and whose histograms are concatenated to act as the face feature. The chi-squared between two sub-block histograms is calculated as the distance for dissimilarity measurement. Votes for these 14 sub-blocks matching between the unknown user and that in the database are counted for user recognition. In addition to face recognition, the AFR can also perform self-learning function to update face models. To achieve active face recognition, it has to recognize users under different lightings and different locations. Once recognized one user, the AFR select sub-blocks with medium similarity measure to update the corresponding ones in the face model. Experiments verified that the proposed AFR can achieve high accuracy and robustness in recognize multi-users with multi-depth of field.

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