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系統識別號 U0002-0408201112132500
中文論文名稱 基於ASM之人臉合成系統
英文論文名稱 Facial Aging Synthesis System Based on ASM
校院名稱 淡江大學
系所名稱(中) 電機工程學系碩士在職專班
系所名稱(英) Department of Electrical Engineering
學年度 99
學期 2
出版年 100
研究生中文姓名 邱瑞伸
研究生英文姓名 RUI-SHEN QIU
學號 798440151
學位類別 碩士
語文別 中文
口試日期 2011-07-15
論文頁數 76頁
口試委員 指導教授-謝景棠
中文關鍵字 ASM 主動形狀模型  Sobel 邊緣偵測  SVM 支援向量機  K-mean 分類器  Log-Gabor小波 
英文關鍵字 Active Shape Model(ASM)  Sobel edge detection  Support Vector Machines(SVM)  K-mean Classifier  Log-Gabor wavelets 
學科別分類 學科別應用科學電機及電子
中文摘要 本文提出一套自動年齡合成系統,可以自動估算輸入影像之年齡層,並執行臉型分類、五官對準及年齡皺紋之合成。自動化方式係以輸入影像經計算機預測之年齡,為合成影像的年齡基準,並整合ASM 演算法所得特徵點,為人臉影像校正及五官定位之基準,然後利用分類方法於資料庫中尋找最相似參考臉型後,以Log-Gabor小波抓取紋理特徵的方法,將人臉影像之年齡紋理解析出,從而合成出一系列不同年齡時期之影像。
英文摘要 This paper presents an automated synthesis system that can automati-cally estimate the age of the input image and perform facial classification, facial targeting and synthesis wrinkles of human ages. The computer auto-matically prediction the age of test image, that be the basis to synthetic im-ages, and will search the feature points of the face image by ASM algorithm, that will be the reference for facial component alignment. After that, we use the classifier to find the most similarly reference image in the database, and analysis the texture of the test image and reference image with Log-Gabor wavelet that can extraction the texture features about aging in-formation and wrinkle. Finally, we can simulate a series of facial images in the different period that will be younger or older than the test image.
論文目次 目錄
致謝 I
中文摘要 III
英文摘要 IV
目錄 V
圖目錄 VII
第1章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 1
1.3 論文架構 2
第2章 文獻探討 4
2.1 基於非負矩陣分解演算法之人臉影像預測 4
2.1.1 非負矩陣分解演算法 4
2.1.2 運用於人臉影像預測之方法 6
2.2 基於多層解析動態模型之人臉影像預測 12
2.2.1 多層解析模型建立 12
2.2.2 動態模型建立 16
2.2.3 人臉老化模擬 18
2.3 基於Log-Gabor小波分析之人臉影像預測 23
2.3.1 AdaBoost演算法 23
2.3.2 ASM主動形狀模型 28
2.3.3 Log-Gabor小波分析 34
2.3.4 人臉老化影像預測 40
第3章 系統架構 44
3.1 影像前處理 46
3.1.1 特徵點萃取 46
3.1.2 旋轉校正 48
3.1.3 尺寸校正 49
3.2 臉型資料庫建立 51
3.2.1 年齡判斷 51
3.2.2 臉型分類 54
3.3 影像年齡合成 57
3.3.1 資料庫搜尋 57
3.3.2 人臉影像年齡模擬 58
第4章 實驗與討論 60
4.1 實驗環境 60
4.2 濾波器參數設定 60
4.3 實驗結果 67
第5章 結論與未來研究方向 71
参考文獻 72

圖2.1 許志維及張軒庭[7]應用NMF在人臉影像預測之方法 7
圖2.2 許志維及張軒庭[7]應用NMF訓練人臉基底影像之方法 9
圖2.3 許志維及張軒庭[7]應用NMF重建人臉影像之方法 10
圖2.4 許志維及張軒庭[7]應用NMF在人臉影像預測之模擬結果 11
圖2.5 Jinli Suo等人[10]提出之多層解析模型 14
圖2.6 Jinli Suo等人[10]提出之多層解析模型的部分樣本 15
圖2.7 Jinli Suo等人[10]提出之多層解析動態模型 16
圖2.8 Jinli Suo等人[10]提出多層解析動態模型之元件替換範例 20
圖2.9 Jinli Suo等人[10]提出多層解析動態模型之皺紋統計範例 21
圖2.10 Jinli Suo等人[10]提出多層解析動態模型之皺紋模擬結果 22
圖2.11 Jinli Suo等人[10]提出多層解析動態模型之人臉老化模擬結果 22
圖2.12 范聖恩[16]之Adaboost訓練演算法流程圖解 26
圖2.13 Paul Viola與Michael Jones[17]提出矩型特徵 27
圖2.14 Paul Viola與Michael Jones[17]提出之層疊分類原理 28
圖2.15 PDM控制點說明示意圖 29
圖2.16 主成份分析(PCA)示意圖 32
圖2.17 何景堂[21]運用Gabor filter萃取人臉紋理結果 36
圖2.18 頻率域中,Log-Gabor小波頻寬之形狀表示圖 39
圖2.19 空間域中,Log-Gabor小波頻寬形狀之實數表示圖 39
圖2.20 空間域中,Log-Gabor小波頻寬形狀之虛數表示圖 40
圖2.21 李祐承[13]及潘俊瑋[14]提出之人臉影像老化合成流程 41
圖2.22 李祐承[13]基於Log-Gabor小波提出之人臉影像老化結果 42
圖2.23 潘俊瑋[14]基於Log-Gabor小波提出之人臉影像老化結果 43
圖2.24 本文提出之系統方塊圖 45
圖3.1 ASM模型萃取75特徵點 47
圖3.2 特徵點描繪人臉輪廓及臉部元件之形狀及位置 47
圖3.3 內眼角不變性之旋轉校正 49
圖3.4 利用ASM特徵點之尺寸校正 50
圖3.5 改良李祐承[13]之年齡判斷過程 52
圖3.6 臉部元件位置關聯資訊之特徵點 52
圖3.7 臉部元件分類示意圖 52
圖3.8 資料庫搜尋示意圖 52
圖3.9 人臉影像年齡模擬方塊圖 52
圖4.1 不同中心頻率下,以Log-Gabor重建人臉影像 61
圖4.2 中心頻率(f0)及其8個方向(orientation,θ)之關係示意圖 63
圖4.3 中心頻率(f0)及其標準差(σf=0.05至0.30)之關係示意圖 64
圖4.4 中心頻率(f0)及其標準差(σf=0.35至0.60)之關係示意圖 65
圖4.5 尺度大小(scale)之紋理合成示意圖 66
圖4.6 年輕影像測試 68
圖4.7 中年影像測試 69
圖4.8 老年影像測試 70
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