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研究生:簡廷任
研究生(外文):Ting-Ren Jian
論文名稱:使用卷積神經網路和3DLBP特徵進行人臉識別
論文名稱(外文):Face Recognition Based on Convolutional Neural Network and 3DLBP Feature
指導教授:汪順祥
指導教授(外文):Shuenn-Shyang Wang
口試委員:汪順祥
口試委員(外文):Shuenn-Shyang Wang
口試日期:2019-01-25
學位類別:碩士
校院名稱:大同大學
系所名稱:電機工程學系(所)
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:英文
論文頁數:27
中文關鍵詞:人臉識別卷積神經網絡三維局部二值模式支持向量機
外文關鍵詞:Face RecognitionConvolutional Neural Network3D Local Binary PatternsSupport Vector Machines
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由於RGB影像人臉識別在處理姿勢與光照變化的效能不佳,近年來已經有許多文獻提出以深度影像進行人臉識別的方法,然而深度影像識別仍有著無法處理的情況,像是大量的表情變化。本論文結合RGB特徵和深度特徵提出一種RGB-D人臉識別方法,並經由實驗分析辨識結果,最後與RGB人臉識別方法以及深度人臉識別方法做比較。所提之方法使用卷積神經網絡(CNN)提取RGB特徵,使用3D Local binary patterns (3DLBP)提取人臉深度特徵,最後藉由Support Vector Machines(SVM) 結合RGB特徵與深度特徵進行人臉識別。實驗中我們使用Python實現所提出的RGB-D人臉識別方法,並以EURECOM人臉資料庫來做測試。
In recent years, many face recognition methods using depth images are presented, because RGB face recognition is not effective to deal with the variations of posture and illumination. However, depth face recognition still suffers some situations that can’t be handled, such as a large number of expression changes. In this thesis, we propose a RGB-D face recognition method and compare the recognition results with the RGB face recognition method and the depth face recognition method. The proposed method used Convolutional Neural Network (CNN) to extract facial RBG features by training, and used the technique of 3D Local binary patterns (3DLBP) to extract facial depth features. Finally, by combing with RBG features and depth features, Support Vector Machines (SVM) was applied for face recognition. Our RGB-D face recognition method was implemented using Python and the experiments were carried out utilizing the EURECOM Face Dataset.
Chinese Abstract i
English Abstract ii
Tables of Contents iii
List of Tables v
List of Figures vi
CHAPTER 1 Introduction
1.1 Background 1
1.2 Organization of the Thesis 2
CHAPTER 2 Face Recognition
2.1 RGB Face Recognition 3
2.2 Depth Face Recognition 4
CHAPTER 3 Convolutional Neural Network for RGB Face Images
3.1 Introduction 5
3.2 RGB+CNN method for RGB face images 12
CHAPTER 4 3D Local Binary Patterns for Depth Face Images
4.1 Introduction 13
4.2 3DLBP+SVM method for depth face images 15

CHAPTER 5 Proposed RGB-D Face Recognition Method
5.1 Introduction 16
5.2 Proposed Method 16
5.3 Experimental Results 17
5.3.1 EURECOM database 17
5.3.2 Training set 18
5.3.3 The Experiment 1 19
5.3.4 The Experiment 2 20
5.3.5 The Experiment 3 20
5.3.6 The Experiment 4 21
CHAPTER 6 Conclusion 23
References 24
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[10] G. G. Gordon, “Face recognition based on depth maps and surface curvature,” Proc. SPIE 1570, Geometric Methods in Computer Vision, pp.234-247, September 1991.
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[12] H. Mohammadzade and D. Hatzinakos, “Iterative Closest Normal Point for 3D Face Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp.381-397, February 2013.
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