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系統識別號 U0026-2707201014014900
論文名稱(中文) 多視角視訊人臉辨識系統
論文名稱(英文) A Multiview Video Face Recognition System
校院名稱 成功大學
系所名稱(中) 電腦與通信工程研究所
系所名稱(英) Institute of Computer & Communication
學年度 98
學期 2
出版年 99
研究生(中文) 陳湘宜
研究生(英文) Hsiang-Yi Chen
學號 q3697448
學位類別 碩士
語文別 中文
論文頁數 108頁
口試委員 指導教授-楊家輝
口試委員-王駿發
口試委員-連震杰
口試委員-杭學鳴
口試委員-章定遠
中文關鍵字 人機互動  電腦視覺  人臉辨識  主成分分析  線性鑑別分析 
英文關鍵字 human computer interaction  computer vision  face recognition  principal component analysis  linear discriminant analysis 
學科別分類
中文摘要 人機互動(Human-Computer Interaction)近年的蓬勃發展,電腦視覺
(Computer Vision)運用於監視系統上,在我們的生活中也逐漸扮演重要的
角色。針對人臉辨識的研究,本論文分別探討單張人臉影像辨識與視訊
人臉影像辨識的效能及表現,以及使用多視角的人臉視訊影像的資料豐
富性來校正非正臉影像以提高辨識率。
在單張人臉影像辨識環境下,相較於Viola-Jones 人臉偵測器,本論
文首先提出一快速人臉偵測演算法,實驗結果證明我們的方法可提高偵
測速度並有較低的誤判率。然後對於視訊人臉辨識,我們使用多攝影機,
利用特徵臉(Eigenface)、線性鑑別分析(LDA),分析測試影像數量、人臉
角度以及非正臉影像校正後的影像對辨識效能的影響。分別使用AT&T、
Stereo face 人臉資料庫,以及自製的多視角人臉視訊資料庫進行實驗。
英文摘要 Human-computer interaction (HCI), has been rapidly developed in recent
years. Computer vision has been used in surveillance systems, and it
gradually plays an important role in our lives. In the research of face
recognition, in this thesis, we discuss the performance of single-based and
video-based face recognition respectively. To improve the recognition rate, we
use the multi-view video face images to synthesize a virtual frontal face.
In the still image face recognition, in this thesis, we first present a fast face
detector. To compare with the face detector of Viola-Jones, experimental
results show that our method can improve the detection speed and reduce
false alarm rate. For the video-based face recognition, we then use the PCA
and LDA to analyze the number of test images, the different views of face and
the frontal views generated from non-frontal images, which affect the
performance of face recognition. Respectively, we used the AT&T, Stereo face
database and our multi-view face database to do experiments and validations.
論文目次 摘 要 i
Abstract ii
誌 謝 iii
目 錄 iv
表目錄 viii
圖目錄 x
第 1 章 簡 介 1
1.1 研究背景 1
1.2 研究目的與動機 2
1.3 人臉辨識系統相關技術 3
1.4 辨識系統流程簡述 5
1.5 論文章節概要 6
第 2 章 相關研究 8
2.1 單張影像之人臉偵測 8
2.1.1 積分影像(Integral Image) 9
2.1.2 Haar特徵與Adaboost演算法 10
2.1.3 串接分類器(Cascade of Classifiers) 11
2.2 視訊影像之人臉追蹤 12
2.2.1平均位移演算法 (MeanShift Algorithm) 13
2.2.2連續適應性平均位移演算法 (CamShift Algorithm) 15
2.3 非正臉影像校正 17
2.3.1主成份分析法(Principal Component Analysis, PCA) 17
2.3.2主動形狀模型(Active Shape Model, ASM) 18
2.4 人臉辨識 20
2.4.1 主成份分析法(Principal Component Analysis, PCA) 21
2.4.2 線性鑑別分析法(Linear Discriminant Analysis, LDA) 25
2.4.3 距離量測法 32
第 3 章 偵測與辨識 34
3.1 快速人臉偵測器 35
3.1.1膚色偵測 (Skin Color Detecting) 36
3.1.2二元膚色面積(Binary Skin Area Ratio, BSAR) 38
3.1.3適應性區塊跳躍(Adaptive Block Jumping, ABJ) 40
3.2 提出的非正臉影像校正 44
3.2.1 Delaunay 三角化分割人臉特徵 45
3.2.2仿射轉換(Affine Transformation) 49
3.2.3 非正臉影像校正作法示意圖 57
3.3 提出的視訊影像辨識 58
3.4 人臉辨識系統流程圖 60
第 4 章 實 驗 62
4.1 人臉資料庫 62
4.1.1 AT&T 63
4.1.2 Stereo face 資料庫 63
4.1.3 自建多視角視訊人臉資料庫 64
4.1.3.1系統軟硬體環境界面建置 64
4.1.3.2拍攝方法介紹 67
4.1.3.3多視角視訊人臉資料庫範例 69
4.1.4 人臉資料庫討論與比較 72
4.2 實驗結果之分析與比較 72
4.2.1 實驗設計說明 72
4.2.2實驗結果呈現與分析討論 73
4.2.3實驗方法比較 101
第 5 章 結論與未來展望 103
5.1 結論 103
5.2 未來展望 104
參考文獻 105
作者簡歷 108
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