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研究生:林咸仁
研究生(外文):Hsien-Jen Lin
論文名稱:改良線性鑑別式分析在少量訓練樣本下之人臉辨識研究
論文名稱(外文):On Improving Linear Discriminant Analysis for Face Recognition with Small Sample Size Problem
指導教授:簡仁宗簡仁宗引用關係
指導教授(外文):Jen-Tzung Chien
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
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:2002
畢業學年度:90
語文別:中文
論文頁數:79
中文關鍵詞:主成分分析訓練樣本數不足問題線性鑑別式分析人臉辨識
外文關鍵詞:Small Sample Size ProblemLinear Discriminant AnalysisPCAPrincipal Components AnalysisFace RecognitionLDA
相關次數:
  • 被引用被引用:26
  • 點閱點閱:1000
  • 評分評分:
  • 下載下載:293
  • 收藏至我的研究室書目清單書目收藏:2
在人臉辨識的相關研究中,線性鑑別式分析(Linear Discriminant Analysis,LDA)是一種常見的線性轉換技術,可以求出一組具鑑別性的特徵參數。然而這個方法在訓練樣本數不足(small sample size)時,會因為矩陣的奇異性問題,而導致無法實現。針對這個問題,本篇論文提出一種改良式的線性鑑別式分析方法:先將原始的特徵參數經由線性轉換投射至一個保留相同散佈程度的特徵空間,在此空間內的類別內散佈矩陣(Within-class scatter matrix)沒有奇異性(Singularity)的問題。若原始類別內散佈矩陣的秩(Rank)為r,則該轉換矩陣是由原始類別內散佈矩陣前r大特徵值所對應之特徵向量所組成。最後再對新的特徵參數做線性鑑別式分析。經由五種不同人臉資料庫實驗的結果,我們可以發現在樣本數目足夠的情形下,辨識率明顯地比原始的LDA提昇許多,而在訓練樣本數不足時也在較少的訓練成本下能維持不錯的辨識效果。
In the literature of face recognition, LDA (Linear Discriminant Analysis) is a popular linear transformation technique to extract the discriminant feature vector. However, this technique may occur the singularity problem on calculating the inverse of the within-class scatter matrix when the training sample size is small. To overcome the problem, an improved linear discriminant analysis is proposed in this thesis. The proposed method aims to transform the original feature vector to a new feature space with the same degree of scattering and without the singularity problem. Then, We use the LDA to extract the new feature vector. In the experiments on five face databases, the results show that the recognition rates of the proposed method is significantly better than other LDA-based techniques when training data are sufficient. In case of very limited training data, the proposed method achieves desirable recognition performance with moderate training cost.
摘要
ABSTRACT
目錄 1
第一章 緒論 6
1.1 研究動機 6
1.2 論文主要內容 8
1.3 人臉辨識系統流程 9
1.4 論文架構 10
第二章 相關研究探討 12
2.1 小波轉換 12
2.2 PCA轉換 15
2.2.1 基本運作原理 15
2.2.2 PCA轉換應用在辨識上不足的地方 20
2.3 LDA轉換 21
2.3.1 基本運作原理 21
2.3.2 LDA轉換的缺點 26
2.3.3 LDA轉換的延伸做法 27
第三章 改良式LDA轉換 35
3.1 線性代數在解決樣本數不足的LDA轉換上之應用 35
3.2 改良式LDA轉換 38
第四章 實驗 43
4.1實驗硬體設備 43
4.2人臉資料庫 43
4.2.1 ORL人臉資料庫 43
4.2.2 中研院人臉資料庫 44
4.2.3 Yale人臉資料庫 44
4.2.4 Stirling人臉資料庫 45
4.2.5 MHMC人臉資料庫 46
4.3 實驗流程說明 46
4.3.1 訓練參數模型 46
4.3.2 測試流程 48
4.4 實驗結果 50
4.4.1 不同人臉資料庫辨識結果比較 50
4.4.2 不同方法訓練時間比較 58
第五章 Demo系統 61
第六章 結論與未來研究方向 63
6.1結論 63
6.2未來研究方向 65
參考文獻 67
附錄一 中研院人臉資料庫部分人臉影像 72
附錄二 ORL人臉資料庫部分人臉影像 73
附錄三 Yale人臉資料庫全部人臉影像 74
附錄四 Stirling人臉資料庫部分人臉影像 76
附錄五 MHMC人臉資料庫部分人臉影像 77
附錄六 資料庫影像及其行向量分解、列向量分解圖 78
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