臺灣博碩士論文加值系統

在本論文，我們提出一個新的學習架構,Direct Combined Model(DCM),來分析及擷取兩個相關多維度變數之間的關係。DCM尋找一個最佳的結合特徵空間來詮釋兩變數的相對關係：同時，也從個別變數取出有意義的特徵。當只有一個的變數可獲得，而另一個變數是不可觀察時，我們利用基於DCM的特性發展出一個特徵轉換技術。由於DCM具有轉換兩各相關特徵空間的能力，本論文裡。我們藉由三個應用，包含：臉部特徵點偵測、人臉素描合成、人臉遮蔽回復，來闡述DCM的有效性。
在臉部特徵點偵測應用裡，DCM有效地建立一個單一、結合的eigenspace來描述臉部特徵點(例如：人臉的形狀)和輸入紋理資訊(例如：人臉影像)之間的關係，因此保留了兩者相依性中有意義的部分。DCM利用已學習的臉部特徵點和輸入紋理資訊關係，發展一個預測器去偵測人臉影像的臉部特徵點。以往對重建人臉形狀的研究，大都嚴重依賴紋理重建結果的品質，換句話說，容易受到輸入影像是否遮蔽和光線變化的影響。實驗顯示DCM能精確的重建人臉形狀，即使輸入影像有不適合的遮蔽或光線變化，也不需要去回復損失的紋理資訊。
自動合成一張人臉影像的人臉素描是一個很大的挑戰，因為人臉影像存在廣範圍的角度、表情、大小變化和不同程度的遮蔽或光線變化。尤其，當我們想要模擬特定畫家的繪畫風格和筆觸來合成人臉素描時，問題將變得更複雜。在自動人臉素描系統裡，我們利用DCM來學習畫家的人臉素描作品模擬以產生具畫家繪畫風的合成素描。此系統有幾個有三個主要的優點：第一，DCM同時考慮人臉的區域細部特徵和人臉整體的形狀，因此合成出來的結果更精確的模仿藝術家誇張的筆觸。第二，雖然樣本資料庫(training database)中只有含正面的自然表情影像，此系統卻能夠成功的合成不同的人臉角度、注視方向和人臉表情。第三，之前的研究大都嚴重依賴紋理重建結果的品質，容易受到輸入影像是否遮蔽和光線變化的影響。即使輸入影像不完整，DCM仍能成功的合成栩栩如生的人臉素描，並且不需要回復損失的紋理資訊。
在第三個人臉遮蔽回復的應用，我們提出一個以DCM為基礎的particle filter方法來解決人臉遮蔽回復以及遮蔽人臉的特徵點偵測。我們首先推導一個Bayesian Network架構將遮蔽人臉恢復及他的特徵點偵測整合在一起，並利用一個particle粒子點的集合來代表recovery objective function的複雜的機率分佈。在此定義裡，每一個particle粒子為一張校準後的人臉影像；對於每一個粒子，被遮蔽的人臉區域首先需要被偵測出來，然後利用沒有被遮蔽區域的人臉來回復遮蔽區域。藉由人臉整體形狀的限制，被回復的影像(即particles粒子)不易受影像雜訊影響，同時他們幾何校正參數也會被精確地重新計算。我們的實驗結果顯示被回復的影像是大致上都接近真實結果，並且沒有人為的涉入。

In this thesis, we present a new learning framework, direct combined model (DCM), for measuring the relationship between two related multidimensional variables. DCM finds one combined feature space to optimize their corresponding correlations, and, at the same time, it extracts the significant features of the individual variables. When only one variable is observed (known) and the other variable is hidden (unknown), a feature transformation, called DCM transformation, is computed with the aid of the combined model, DCM. DCM transformation estimates the transforms between the two related random variables and their significant correlations are directly considered. The effectiveness of DCM approach is demonstrated in three applications including facial feature point detection, face sketch synthesis, and occluded face recovery.
In the facial feature point detection framework, DCM efficiently models the correlation between the facial feature points (i.e., the facial shape) and the input texture information (i.e., the facial image) in a single combined eigenspace, which preserves the significant components of their dependency. Then, a DCM estimator is developed for estimating the facial feature points from the input image, where the learned correlation is explicitly take into account. Previous proposals for reconstructing facial shapes are heavily reliant on the quality of the texture reconstruction results, which are highly sensitive to occlusion and lighting effects in the input image. Our experiments show that the DCM approach accurately reconstructs the facial shape without the need to restore the texture information lost as a result of unfavorable occlusion or lighting conditions.
Automatically synthesizing the facial sketches of a facial image is highly challenging because facial images typically exhibit a wide range of poses, expressions and scales, and have differing degrees of illumination and/or occlusion. When facial sketches are synthesized in the unique sketching style of a particular artist, the problem becomes even more complex. The automatic facial sketch synthesis framework based on the DCM algorithm has three major advantages. First, DCM approach takes the local details of each facial feature and the global geometric structure of the face into account; thus, the synthesized sketches more accurately mimic the caricatures drawn by the artist. Second, although the training database contains only full-frontal facial images with neutral expressions, sketches with a wide variety of facial poses, gaze directions, and facial expressions can be successfully synthesized. Third, previous synthesizing proposals are heavily reliant on the quality of the texture reconstruction results, which are highly sensitive to occlusion and lighting effects in the input image. DCM approach accurately produces lifelike synthesized facial sketches without the need to restore the texture information lost as a result of such unfavorable conditions.
With regards to the third application, the occluded face recovery framework, we present a DCM-based particle filter solution for recovering and aligning the occluded facial image without the aid of manual face alignment. We first derive a Bayesian framework to unify the recovery stage with face alignment, where the complex distribution of the recovery objective function is represented by a particle set. In this work, a particle is an aligned facial image. Its occluded facial regions are first detected and then recovered by two basic DCM modules, namely, a shape recovery module and a texture recovery module. Each module models the occluded and non-occluded regions of the facial image in a DCM, which preserves the correlations between the geometry of the facial features and the pixel gray values, respectively, in the two regions. As a result, when shape or texture information is available only for the non-occluded region of the facial image, the optimal shape and texture of the occluded region can be reconstructed via a process of Bayesian inference. To enhance the quality of the reconstructed results, the shape reconstruction module suppresses the effects of biased noises, so that it is robust to facial feature point labeling errors. Furthermore, the texture reconstruction module recovers the texture of the occluded facial image by synthesizing the global texture image and the local detailed texture image. Our extensive experimental results demonstrate that the recovered images are quantitatively closer to the actual images without manual involvement.

中文摘要 IV
Abstract VI
誌 謝 IX
List of Figures X
List of Tables XIV
Table of Contents XV
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Learning-based Transformation 2
1.3 Overview of Our Works 6
1.4 Thesis Outline 9
Chapter 2 Direct Combined Model 12
2.1 Introduction 12
2.1.1 Separated Models vs. Combined Model 12
2.1.2 Overview of Our Solution 13
2.2 Direct Combined Model Learning 14
2.2.1 Combined Space Modeling 14
2.2.2 Combined Model-based Transformation 15
2.2.3 Procedure of Direct Combined Model Learning 20
Chapter 3 Facial Feature Point Detection Using Direct Combined Model 23
3.1 Introduction 23
3.1.1 Overview of DCM-based Framework 24
3.2 Facial Feature Point Detection Using DCM 25
3.2.1 Facial Feature Point Detection System 26
3.2.2 Analysis of Texture-to-Shape DCM 30
3.2.3 Procedure of DCM-based Feature Points Estimation 30
3.3 Experimental Results 31
Chapter 4 Facial Sketch Synthesis Using Direct Combined Model 39
4.1 Introduction 39
4.1.1 Overview of DCM-Based Synthesis Framework 41
4.2 Face Sketch Synthesis Using DCM 42
4.2.1 Face Sketch Synthesis System 45
4.2.2 Analysis of Shape-to-Exaggerated-Shape DCM 49
4.2.3 Analysis of Texture-to-Sketch-Texture DCM 51
4.2.4 Procedure of DCM-based Facial Sketch Synthesis 51
4.3 Experimental Results 53
Chapter 5 Direct Combined Model-based Bayesian Alignment and Recovery for Occluded Face 62
5.1 Introduction 62
5.1.1 Overview of DCM-based Solution 64
5.2 DCM-based Markov Network 65
5.3 DCM -based Bayesian Alignment and Recovery 66
5.3.1 Unified Probability Model 67
5.3.2 Occluded Face Alignment and Recovery System 69
5.3.3 Face Alignment by DCM-based Particle Filter 71
5.3.4 Shape Recovery Module 74
5.3.5 Texture Recovery Module 76
5.4 Experimental Results 79
Chapter 6 Conclusions 81
Reference 83
作者簡歷 (Author’s Biographical Notes) 93
著作目錄 (Publications) 95

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