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系統識別號 U0026-2507201616093400
論文名稱(中文) 在亮度變化的情況下使用基於小波轉換的自適性影像強化進行人臉辨識
論文名稱(英文) Adaptive Wavelet-Based Image Enhancement for Face Recognition under Variable Illumination Conditions
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 104
學期 2
出版年 105
研究生(中文) 黃啓倫
研究生(英文) Chi-Lun Huang
學號 N26034998
學位類別 碩士
語文別 英文
論文頁數 45頁
口試委員 指導教授-賴源泰
口試委員-戴自強
口試委員-曾照峰
口試委員-林宏益
口試委員-沈祖望
中文關鍵字 人臉辨識  離散小波轉換  亮度品質因素  主成分分析  線性判別分析 
英文關鍵字 Face Recognition  Discrete Wavelet Transform  Luminance Quality Index  Principal Components Analysis  Linear Discriminant Analysis 
學科別分類
中文摘要   人臉辨識在現今的生活當中扮演著非常重要的角色,並且廣泛的應用在各個不同的領域,像是信用卡認證、保安系統和出入境身分辨認等。而人臉辨識技術可能會因為一些外在的因素使得辨識錯誤,例如亮度的變化、姿勢或表情的變化、影像遮蔽等原因。
  本篇論文主要以亮度的變化為探討主題,並提出一個前處理方法來解決亮度變化所造成的辨識錯誤。提出的方法是利用小波轉換將影像的高頻與低頻成分分開處理,我們對原影像亮度進行正規化,進而降低亮度變化對低頻成分所造成的影響,同時對影像的高頻成分做增強,以提高人臉的輪廓。接著使用主成分分析降維以及使用線性判別分析法擷取影像特徵,最後將擷取到的特徵與訓練的特徵做比較,來達到辨識的目的。
英文摘要   Face recognition plays an important role in nowadays. It is critical in a wide range of applications such as mug-shot database matching, credit card verification, security system, and scene surveillance. Some adversely external factors probably cause the recognition result incorrect. Changes in lighting conditions, facial expressions and pose, occlusion are the main obstacle for face recognition.
  In this thesis, variable illumination conditions are considered. We propose a preprocessing scheme to reduce error rates under uncontrolled illumination conditions. Wavelet Transform (WT) is used to decompose an image into low and high frequency components. To deal with low and high frequency components separately, we can alleviate the affects cause by variable illumination conditions. Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) are used to extract facial feature.
論文目次 Chapter 1 Introduction 1
1.1 Methodology 1
1.1.1 Feature Based Methods 2
1.1.2 Generative Methods 2
1.1.3 Holistic Methods 3
1.2 Preprocessing 4
1.3 Features Extraction 4
1.4 Classifier 5
1.5 Thesis Organization 5
Chapter 2 Preliminary 6
2.1 Preprocessing 6
2.1.1 Histogram Equalization (HE) 6
2.1.2 Discrete Wavelet Transform (DWT) 8
2.1.3 Universal Quality Index (UQI) 10
2.2 Features Extraction 11
2.2.1 Principal Components Analysis (PCA) 11
2.2.2 Linear Discriminant Analysis (LDA) 15
2.3 Classifier 19
2.3.1 Euclidean Distance 19
2.3.2 K-Nearest Neighbors (KNN) 19
Chapter 3 Proposed Method 21
3.1 Framework 21
3.2 Adaptive Wavelet-Based Image Enhancement (AWIE) 22
3.2.1 Region-Based Histogram Equalization (RHE) 22
3.2.2 Design of DWT 25
3.2.3 Image Enhancement 27
3.2.4 Inverse Discrete Wavelet Transform (IDWT) 29
Chapter 4 Experimental Results and Discussions 33
4.1 Face Database 33
4.2 Training 34
4.3 Reference Image 35
4.4 Recognition Results 35
4.4.1 Luminance Quality 36
4.4.2 Recognition Results of Proposed Method 37
Chapter 5 Conclusions 42
Reference 43
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