近年來的研究在人臉辨識領域上有長足的進步，同時硬體設備也越來越進步。在這方面的應用也越來越多。我們發現在實際應用上需要解決各種變因對辨識效果的影響。本篇論文以姿態變化為主要探討的方向。用定因素分析作為系統的核心。姿態變化造成的複雜分布，利用多個因素分析的模型去解釋。同時考慮不同姿態下的同一人的影像，應該有潛在的變數能夠解釋這個人的身份，而不受姿態或人臉變因的影響。利用期望值最大化演算法迭代地找出最佳參數。系統在辨識階段，利用各種生成模型去解釋各種資料的生成情形以最可能的模型所對應的情況作為辨識結果。為了加強系統對變異的容忍度，我們採用仿射轉換的正規化方式減少姿態變化的影響，再利用加入多種變因的方式提升系統的辨識能力。

Recently, there is a significant progress in study of face recognition. Meanwhile, hardware technology advances, too. The face recognition has been applied on more areas. In order to make the face recognition practical, it is required to reduce the effect of variations on performance. In this thesis, we focus on the pose variations problem and take Tied Factor Analysis as the core of system. The concept is to explain the complex distribution caused by pose variations with several Factor Analysis models. Moreover, there exists a certain representation for images that at different poses from the same subject without regard to pose and facial variations. In learning process, the EM algorithm is used to find the optimal parameters iteratively. In recognition process, a variety of generative models are designed to explain the different generative procedures of data, and then take the most likely procedure as the result. To increase the tolerance for variations, we use the Affine Transform normalization to reduce the effect of pose variations. And we add various variations to promote the performance.

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