異質(zhì)圖像合成是指對各種不同來源人臉圖像之間進行轉化與合成,比如不同光照變化下相機拍攝的人臉照片、畫家手繪的素描畫像、軟件合成的人臉照片以及紅外成像設備采集到的紅外圖像。近年來,應用在數(shù)字娛樂領域的異質(zhì)圖像合成以及應用在執(zhí)法領域中的素描畫像的合成與識別受到了極大的關注。人臉畫像合成主要是指通過輸入的照片生成相應的素描畫像,主要通過一些合成方法對畫像-照片之間的復雜映射關系進行建模,并利用所學習到的映射關系來合成輸入照片所對應的素描畫像。在理想情況下,合成的畫像或照片圖像應該保留更多的外觀紋理,并且越逼真越好。這樣,它才具有良好的視覺感知質(zhì)量和較高的識別精度。鑒于此,本文基于深度學習方法開展關于人臉畫像合成和識別的相關研究。首先,本文對一些典型的人臉畫像合成方法進行了全面的回顧和比較。目前尚未見關于人臉畫像合成方法的實驗對比和分析的研究。鑒于合成過程與訓練模型相關聯(lián),現(xiàn)有方法可以分為兩大基本類型:數(shù)據(jù)驅(qū)動的方法和模型驅(qū)動的方法。根據(jù)人臉畫像合成過程,數(shù)據(jù)驅(qū)動方法,又稱為基于樣本的方法,一般包含四個步驟:圖像塊表示、近鄰選擇、權重計算以及圖像塊拼接。而模型驅(qū)動方法則直接學習人臉照片和畫像之間... 

【文章來源】：西安電子科技大學陜西省 211工程院校 教育部直屬院校

【文章頁數(shù)】：114 頁

【學位級別】：博士

【文章目錄】：
ABSTRACT
摘要
Nomenclature
ACRONYMS
Chapter 1 Introduction
    1.1 Heterogeneous Image Synthesis
        1.1.1 Digital Entertainment
        1.1.2 Law Enforcement
        1.1.3 Examples of FSS Application
    1.2 History and State-of-the-art Methods
        1.2.1 Conventional State-of-the-art Methods
        1.2.2 Deep Learning-Based Methods
    1.3 Structure of the Thesis
        1.3.1 Main Contents of This Thesis
        1.3.2 Organization of the Thesis
Chapter 2 Comparative Study on Some Typical FSS Methods
    2.1 Introduction
    2.2 Data-Driven Methods
        2.2.1 Subspace Learning-Based Methods
        2.2.2 Sparse Representation-Based Methods
        2.2.3 Probabilistic Graphical Model-Based Methods
    2.3 Model-Driven Methods
        2.3.1 Linear Model-Based Methods
        2.3.2 Nonlinear Model-Based Methods
    2.4 Experiments and Analysis
    2.5 Data Preparation
        2.5.1 Database Specification
        2.5.2 Representative Methods Settings
    2.6 Data Analysis
        2.6.1 Face Sketch Synthesis
        2.6.2 Image Quality Assessment
        2.6.3 Face Sketch Recognition
    2.7 Chapter Summary
Chapter 3 A Novel FSS Approach with GAN and CNN Models
    3.1 Introduction
    3.2 Related Work
        3.2.1 Previous FSS Methods
        3.2.2 Deep Learning-Based FSS Methods
    3.3 Proposed Method
        3.3.1 Fully Functional Framework
        3.3.2 Coarse Estimation Through GAN Model
        3.3.3 Fine Estimation Through CNN Model
        3.3.4 Algorithm For Proposed Method
    3.4 Chapter Summary
Chapter 4 Performance Evaluation of FSS Methods
    4.1 Introduction
    4.2 General Image Quality Assessment (IQA)
        4.2.1 Subjective Evaluation
        4.2.2 Objective Evaluation
        4.2.3 Structural Similarity Index Metric (SSIM)
    4.3 Weighted SSIM For Synthesized Sketch Quality Assessment
        4.3.1 Gaussian Approach
        4.3.2 Human Specified Technique
    4.4 Sketch Recognition For Performance Evaluation of FSS
        4.4.1 Principle Component Analysis (PCA)
        4.4.2 Eigenface Technique
        4.4.3 Linear Discriminant Analysis (LDA)
        4.4.4 Null space Linear Discriminant Analysis (NLDA)
    4.5 Chapter Summary
Chapter 5 Experimental Results and Analysis
    5.1 Introduction
    5.2 Parameter Specification
        5.2.1 For GAN Model
        5.2.2 For CNN Model
    5.3 Face Sketch Synthesis
    5.4 Camera Captured Face Photos Database
    5.5 Objective Image Quality Assessment
    5.6 Face Recognition
    5.7 Chapter Summary
Chapter 6 Concluding Remarks and Future Work
    6.1 Concluding Remarks
    6.2 Future Work
References
Acknowledgements
Author Profile



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