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系統識別號 U0026-1607201417305900
論文名稱(中文) 使用Retinex演算法作光線變化下之人臉辨識
論文名稱(英文) Face Recognition under Illumination Variation by Using Retinex Algorithm
校院名稱 成功大學
系所名稱(中) 電機工程學系
系所名稱(英) Department of Electrical Engineering
學年度 102
學期 2
出版年 103
研究生(中文) 林家緯
研究生(英文) Jia-Wei Lin
學號 N26001351
學位類別 碩士
語文別 英文
論文頁數 60頁
口試委員 指導教授-賴源泰
口試委員-戴自強
口試委員-林宏益
口試委員-葉佳楠
中文關鍵字 人臉辨識  Retinex演算法  區域二位元描述 
英文關鍵字 face recognition  local binary patterns  linear discrimination analysis 
學科別分類
中文摘要 目前人臉辨識已經是非常熱門的研究主題,因為可以廣泛地應用在許多領域當中,例如:資訊安全、警監系統、人機互動、犯罪人員辨識。
人臉辨識的準確率容易受到光線變化,或是人臉表情、頭部姿勢改變等影響。本篇論文提出一個架構,將人臉影像以Retinex 演算法進行陰影去除後,再以區域二位元編碼方式描述特徵,整體和局部特徵擷取的架構解決了因為光線變化而導致辨識率下降的問題。
由實驗結果得知,在有限的訓練影像下,經由陰影去除以及使用整體和區域特徵擷取提高了人臉辨識系統的辨識率。
英文摘要 Face recognition has been an active research area due to its wide range of application in information security, video surveillance systems, human-computer interaction, and criminal verification.
Illumination variation, facial expression, and pose variation remain a persistent challenge in face recognition. In the thesis, we proposed a face recognition system which can resolve the problem caused by illumination variation. In our method, Retinex algorithm is adopted to remove the shadow of face firstly, and Local binary pattern is used to describe face feature. We propose global and local discriminative features for face recognition under various facial conditions based on robust feature description and feature extraction.
Experimental results demonstrate that our method can improve face recognition rate when amount of training images limited.
論文目次 Chapter 1 Introduction 1
1.1 Motivation 1
1.2 literature Survey 2
1.3 Organization of The Thesis 6
Chapter 2 Face Recognition System Overview 7
2.1 Image Normalization 7
2.1.1 Retinex Algorithm 7
2.1.2 Single-Scale Retinex 8
2.1.3 Multi-Scale Retinex 9
2.2 Feature Description 11
2.2.1 Histogram Equalization 11
2.2.2 Local Binary Patterns 13
2.2.3 Local Ternary Patterns 14
2.3 Feature Extraction 16
2.3.1 Principal Component Analysis 16
2.3.2 Linear Discriminant Analysis 20
2.3.3 Locality Preserving Projections 24
2.4 Classifier 27
2.4.1 k-Nearest Neighbor 27
2.4.2 Support Vector Machine 28
Chapter 3 Proposed Methods 33
3.1 Proposed Methods Flowchart 33
3.2 Image Normalization 34
3.3 Global and Local Discriminative Feature 35
3.4 Classifier 37
3.5 Global and Local Discriminative Feature Framework 38
3.6 Training Process of Face Recognition 39
3.7 Testing Process of Face Recognition 41
Chapter 4 Experimental Results 43
4.1 Face Image Database 43
4.2 Experience Results 43
4.2.1 Results by using Retinex Algorithm 44
4.2.2 Results of Combination Classifier 47
Chapter 5 Conclusions 55
References 56
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