電腦視覺已普遍運用於監視系統上，在日常生活已經扮演重要的角色。針對人臉辨識的研究。然而，因環境與情境的變化，人臉辨識上有許多問題待改進，本論文分別探討遮蔽問題、燈光變化、表情變化、角度變化以及低解析度影像等問題對人臉辨識的影響，並分別提出解決方法來提高人臉辨識效能。
線性回歸分類法在2010年被提出並應用在人臉辨識上，但是，線性回歸分類法仍然還有問題待解決。為了解決燈光變化問題，我們提出一改進的主成份回歸分類法，它可以克服燈光變化問題而且理論證明它的回歸係數相較於其他回歸分析方法有較小的標準差，所以可以估測較穩定的回歸係數。對於角度變化問題，我們提出一元化回歸分類法，它的目標是最小化全部類別內的投影誤差以找到一最佳投影矩陣。如此一來，一元化回歸分類法的回歸係數在統計上有最小的標準差，而且對於角度變化的人臉辨識可以有較好的效能。
另外，我們結合鑑別力分析及線性回歸分類法而提出一線性鑑別性回歸分類法來克服表情變化的問題，它的目標是最大化類別間的重建誤差和類別別內的重建誤差比率來找到一最有鑑別力的投影矩陣。不僅如此，為了克服低解析度人臉影像問題，我們提出一核線性迴歸分類法來進行低解析度人臉辨識。核線性迴歸分類法是利用核方法將資料投影在較高維度空間以進行線性迴歸分類。
最後，我們提出一通用遮蔽模型來處理遮蔽問題，並使得子人臉隱藏式馬可夫模型可克服部份遮蔽問題，而且可以從部份遮蔽人臉進行人臉模型訓練。實驗結果證明我們的方法可提高人臉辨識率且有較高強健性。

Computer vision has been widely applied in surveillance systems and plays an important role in our lives. However, there are still many problem unsolved in different environments and various applications. In the research of face recognition, we have dealt with the problems including the partial occlusion, illumination variation, different expression, pose variation, and low resolution, respectively by proposing several novel methods.
In 2010, the linear regression classification has been proposed to face recognition. However, it still remains research room for further research and development. For illumination variation problem, we proposed an improved principal component regression classification algorithm which can overcome the multicollinearity problem and has smaller variance of regression coefficients than the existing regression methods so as to estimate reliable regression coefficients for variable lighting face recognition. As for pose variation, a unitary regression classification method is introduced to find an optimal projection by minimizing the total within-class projection error. Therefore, the unitary regression classification has the smallest variance statistically and can attain good performance for face recognition under pose variation. Also, we proposed a linear discrimant regression classification method by embedding the Fisher’s criterion into the linear regression classification. The linear discriminant regression classification method attempts to seek an optimal projection by maximizing the between-class reconstruction error over the within-class reconstruction error and can obtain good performance for face recognition under expression variations. Besides, we proposed a kernel linear regression classification algorithm by using kernel trick to perform linear regression classification in the higher dimensional feature space, which can achieve good results for low resolution face recognition. Furthermore, to handle partial occlusion problem, we proposed a subface hidden Markov model which construct five models for each person and create a universal occlusion model for general occlusion. The proposed subface hidden Markov model coupled with a universal occlusion model can work on the partially occluded face recognition and construct face models from partially occluded face images. Experimental results have shown that the proposed methods can perform better than the related works and possess high robust against problems of partial occlusion, illumination variation, different expression, pose variation, and low resolution, respectively.

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