基於動態線性決策函數之區域圖樣特徵於人臉辨識應用

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洪宗湧(Tsung-yung Hung) 查詢紙本館藏

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資訊工程學系

論文名稱

基於動態線性決策函數之區域圖樣特徵於人臉辨識應用
(Novel Local Pattern Descriptors via Dynamic Linear Decision Function for Face Recognition)

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摘要(中)

在近期人臉辨識系統的相關研究中，注重人臉的表示式，並發掘多種方法去解析人臉在表情與光線變化影響下仍可取得的穩定特徵。其中一種便是區域二元圖樣，其特性包含運算簡單且有穩健的區域紋理特徵，本論文提出的區域紋理特徵便是經由動態線性決策函數，以延伸區域二元圖樣至以向量和以方向純量為基礎的區域紋理特徵，並應用於人臉辨識。首先是區域向量特徵，其提供新的向量表示式與編碼方式(比較空間轉換)，以產生更具有鑑別力的區域紋理特徵。接著是區域方向分類特徵，其利用額外的鄰邊向素以產生八個方向的邊之權重值，再取其極大值與極小值的方式進行編碼。以上所提出的方法皆經實作，並利用FERET，CAS-PEAL，CMU-PIE，Extend Yale B及LFW資料庫，比較區域二元圖樣和現今已有的區域紋理特徵方法。實驗結果證明本論文所提出來的方法，不論是在灰階影像或賈伯特徵，皆優於其他比較的方法。

摘要(英)

Recently, the research in face recognition has been focused on developing a face representation that is designed to generate invariant features for solving facial illumination and expression. Motivated by a simple but powerful local pattern descriptor, Local Binary Pattern (LBP), two novel local pattern descriptors are proposed to extend the LBP to vector-based and directional-based local pattern descriptors via dynamic linear decision function for face recognition. The first descriptor, namely, Local Vector Pattern (LVP), provides a novel vector representation and a coding scheme Comparative Space Transform (CST), which are used to generate more detailed discriminative local features than the other methods. The second proposed descriptor, namely, Local Directional Classifier Pattern (LDCP), computes eight edge response values from extra neighborhood pixels, and these values are used to select the upper and lower bound indices for generating robust complete binary codes. These methods are implemented and compared with existing LBP face recognition systems and other state-of-art local pattern descriptors on FERET, CAS-PEAL, CMU-PIE, Extend Yale B, and LFW databases. Experimental results demonstrate that the proposed methods outperform the other comparative methods with grayscale images and Gabor features as inputs.

關鍵字(中)

★ 區域紋理特徵
★ 區域向量特徵
★ 區域方向分類特徵
★ 動態線性決策函數
★ 比較空間轉換
★ 人臉辨識

關鍵字(英)

★ Local Pattern Descriptors
★ Local Vector Pattern
★ Local Directional Classifier Pattern
★ Dynamic Linear Decision Function
★ Comparative Space Transform
★ Face Recognition

論文目次

Contents　　　v
List of Figures　　　viii
List of Tables　　　xv
1 Introduction　　　1
1.1 Challenges of Face Recognition　　　1
1.2 Contributions　　　2
1.3 Organization of Dissertation　　　2
2 Local Pattern Descriptor　　　4
2.1 Local Binary Pattern　　　4
2.2 Local Derivative Pattern　　　6
2.3 Local Tetra Pattern　　　7
2.4 Local Directional Pattern　　　12
2.5 Local Directional Number Pattern　　　13
3 Local Vector Pattern in High-Order Derivative Space　　　16
3.1 Introduction　　　17
3.2 The Proposed Local Pattern Descriptor　　　19
3.2.1 Local Vector Pattern　　　20
3.2.2 Coding Scheme - Comparative Space Transform　　　23
3.2.3 Measurement of Similarity　　　29
3.3 Extending Local Vector Pattern to High-Order Derivative Space　　　31
3.4 Summary　　　39
4 Local Directional Classifier Pattern Using Prominent Indices　　　40
4.1 Introduction　　　41
4.2 Local Directional Classifier Pattern　　　42
4.2.1 Coding Scheme　　　43
4.2.2 Compass Masks　　　45
4.2.3 Face Descriptor　　　46
4.3 Summary　　　46
5 Results and Discussions　　　48
5.1 Local Vector Pattern in High-Order Derivative Space　　　48
5.1.1 Experimental Results on FERET Database　　　49
5.1.2 Experimental Results on CAS-PEAL Database　　　58
5.1.3 Experimental Results on CMU-PIE Database　　　61
5.1.4 Experimental Results on Extended Yale B Database　　　63
5.1.5 Experimental Results on LFW Database　　　65
5.2 Local Directional Classifier Pattern Using Prominent Indices　　　68
5.2.1 Experimental Results on FERET Database　　　69
5.2.2 Experimental Results on CAS-PEAL Database　　　72
5.2.3 Experimental Results on CMU-PIE Database　　　74
5.2.4 Experimental Results on Extended Yale B Database　　　75
5.2.5 Experimental Results on LFW Database　　　76
6 Conclusions and Future Works　　　78
6.1 Summary and Contributions　　　78
6.2 Future Works　　　78
References　　　80

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指導教授

范國清(Kuo-chin Fan)

審核日期

2014-7-22

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