這篇論文中提出一個以外貌為基底的多角度人臉偵測及角度估計的系統。它是一種由簡單到複雜的架構，配合全體到區域性的特徵使用，並且達到粗略及精細的角度估計。首先，串接式的非人臉移除器在特徵空間中採用全體的資訊達到移除非人臉的測試樣本。在串接移除器的最後一個階段，所包含的人臉模型有隱含人臉角度的資訊，借此做為粗略的角度估計。此粗估的角度像是一個多功器的選擇訊號，將通過串接移除器的測試樣本送至下一個功能狀態－針對不同角度的貝式分類器。此貝式分類器採用區域性的特徵，並且以主成分分析的成分及獨立分析的成分為基底，統計人臉及非人臉樣本的模型。透過統計後建立的模型，分類器將人臉樣本和非人臉樣本做分辨。最後，根據統計後的角度模型，進行精細的角度估計。實驗顯示每個功能狀態的成果，以及整個系統所達到的低錯誤率及高正確率的結果。

　We present an appearance-based technique for not only detecting multi-view faces but also estimating the corresponding poses. Our architecture concerns the global-to-local facial features and estimates the coarse-to-fine pose angle. The system is composed of four subsystems. Firstly, for the cascaded nonface rejecter subsystem, the goal of the nonface rejecters is to reject most nonface patterns (20x20-pixel window) in order to reduce the computational time of remaining two subsystems. Secondly, based on the projection weights of the global facial feature eigenspace, the coarse pose angle of each survived face or nonface patterns is estimated. Thirdly, for the view-based face detector subsystem, the view-based detectors use the joint probability of local features and corresponding positions to model the face. Each local feature projects into the corresponding eigenspace and the ‘residual independent basis’ space. Then Bayes decision rule is applied to judge whether the input pattern contains a face or not. Finally, for the fine pose estimate subsystem, the fine pose angle of the detected face is estimated by using the combination of vector quantization and maximum likelihood probability. According to the experimental results, the accuracy of the face detection and the result of pose estimation are promising.

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