近年來的研究在人臉辨識領域上有長足的進步。我們發現在實際應用上需要解決各種變因對人臉辨識效果的影響。本篇論文以光線變化以及表情變化為主要探討的方向。用定機率線性判別分析作為系統的核心。光線變化與表情變化造成的複雜分布，利用機率線性判別分析的模型去解釋。同時考慮同一人不同張的影像下，應該有潛在的變數能夠解釋這個人的身份，而不受姿態或光線或人臉變因的影響。我們利用了這個生成模型來解釋各種資料的生成情形以最可能的比較可能性結果所對應的情況作為辨識結果。 利用一些資料庫來做實驗: FERET, ORL, 以及 Yale資料庫。

Recently, there is a significant progress in study of face recognition, consequently there are many of face recognition applications appeared nowadays. In order to make the face recognition implemented to real-time system, it is required to reduce the effect of variations for better performance. In my research, I focus on overcome with facial expression variations and illumination variations problems and take Probabilistic Linear Discriminant Analysis as the core of system. The concept is to model the complex distribution caused by those variations with Probabilistic Linear Discriminant Analysis model. In fact, there will be a representation of images that are constant for the same subject, regardless of pose, illumination, and any other variations. We are using these generative models to interpret the generative procedures of data, and then take the most likely matching likelihood result to determine the individual matches. We investigate performance by using the FERET, ORL, and Yale database.

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