臺灣博碩士論文加值系統

近年來因為人臉驗證的廣泛應用及實際需求, 許多研究都致力於真實情境下的人臉驗證。我們可以從網路上蒐集大量的圖像資料, 並從而建立相關的資料庫, LFW 資料庫就是利用這方式建立, 作為評估不同演算法在現實中人臉驗證的表現的資料庫。
在這篇論文裡, 我們提出利用局部區塊的稀疏表示法來描述人臉影像的結構和特徵並把這個稀疏表示法的描述方式應用在人臉驗證上。稀疏編碼在過去的研究中顯示對抗雜訊是很穩健的, 我們從蒐集的人臉影像學習出具代表性的「字典」, 並且透過稀疏編碼利用這個字典來表示影像中的局部區塊。利用學習的字典和稀疏編碼, 我們在幾個資料庫上進行人臉驗證的實驗, 並提升了準確率。

Contents

1. Introduction 1
1.1 Motivation 1
1.2 Problem Description 2
1.3 Related Works 3
1.4 Main Contribution 8
1.5 Thesis Organization 8
2. Proposed method 9
2.1 System overview 9
2.2 Local Patch Sparse Representation Descriptor 10
2.3 Similarity measure and Face verification 19
2.4 Pose normalization by 3D generic model 19
3. Experimental Results 23
3.1 Parameter Setting 24
3.2 Results on CMU PIE dataset 29
3.3 Results on Cohn-Kanade AU-Coded Facial Expression Dataset 30
3.4 Results on LFW Dataset 33
3.5 Pose Normalization 36
4. Conclusion 39
4.1 Summary 39
4.2 Future work 40
References 41

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