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研究生:洪倩玉
研究生(外文):Chien-Yu Hung
論文名稱:建立動態線性鑑別式分析於線上人臉辨識與驗證
論文名稱(外文):Dynamic Linear Discriminant Analysis for Online Face Recognition and Verification
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:2003
畢業學年度:91
語文別:中文
論文頁數:93
中文關鍵詞:人臉偵測線性鑑別式分析人臉驗證假設檢定F分配
外文關鍵詞:face detectionF distributionhypothesis testingface verificationLDA
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在人臉辨識與人臉驗證的相關研究中,線性鑑別式分析(Linear Discriminant Analysis, LDA)是一種常見的線性轉換技術,透過LDA轉換後可以求出一組具有鑑別性的特徵參數。然而應用在線上的人臉辨識與人臉驗證系統上,必須因應資料的更新與刪除等情況,重新訓練LDA的轉換參數,LDA的轉換參數包括了散佈矩陣與轉換矩陣。傳統的LDA需花相當多的時間才能獲得更新後的轉換參數,在本篇論文中針對傳統LDA無法有效動態更新這些參數的缺點,提出了一個動態LDA演算法,利用代數的運算,不但能節省重新訓練的時間,在重新訓練時也只需要保留少數的相關參數,即能無誤差的獲得更新後的轉換參數。另外在人臉驗證的系統上,藉由 LDA與最佳相似度線性轉換(Maximum Likelihood Linear Transformation, MLLT)的結合獲得最佳的線性轉換矩陣,並且應用機率分布的概念對人臉驗證的問題作假設檢定(Hypothesis Testing)根據相似度比值(Likelihood Ratio, LR)為F分配,選擇不同的顯著水準進行假設檢定。本論文提出驗證的方法優於傳統人臉驗證的方法,主要是因為傳統方法採用相似度比值測試(Likelihood Ratio Test, LRT),並用經驗法則手動的調整驗證的門檻值(threshold),本方法可以依照不同的需要,選擇不同的顯著水準進行檢定。實驗中,我們將所提出來的方法應用在中研院人臉資料庫及本實驗室收集的汽車人臉資料庫皆有不錯的效果,我們也實現一套線上動態人臉辨識及驗證展示系統。
Linear Discriminant Analysis (LDA) is a popular linear transformation method for face recognition verification. Using LDA, we can extract the low-dimensional discriminative feature parameter for human faces. In the applications of face recognition and verification, it is usually necessary to enroll the system with new papers and templates. Also, we often need remove the out-of-date persons or templates from the system model. Namely, using the LDA model, the within and between class scatter matrices and the transformation matrices should be recomputed. However, such a recomputation is very time-consuming. To overcome this weakness, a dynamic LDA algorithm is proposed in this paper. Apply this algorithm, we cannot only save a huge amount of computation time but also obtain the updated new parameters with relatively small storage of model parameters. Moreover, in face verification system, we estimate the optimal matrix via by combining the theories of LDA and Maximum Likelihood Linear Transformation (MLLT). We also derive the distribution of likelihood ratio based on MLLT to be the F distribution. Then, the face verification system is carried out via hypothesis testing using different significant levels for F distribution. The advantage of new method is that the verification decision is done according to statistically meaning "significant levels". This superiority is attractive compared to the conventional method using empirical thresholds. In the experiments, we obtain desirable performance using IIS face database and CSIE/NCKU car face database. An online dynamic face recognition and verification demo system is implemented.
摘要
ABSTRACT
第一章緒論6
1.1 研究動機6
1.2 論文主要內容10
1.3 線上人臉辨識暨驗證流程11
第二章系統架構介紹14
2.1 動態人臉偵測14
2.1.1 考慮人臉與背景的關係加快人臉偵測速度14
2.1.2 像素色彩轉換18
2.1.3相似機率計算比對19
2.1.4 取出影像輪廓並二元化、眼睛輪廓特徵判斷19
2.2 小波臉前處理22
2.3 分類原則24
2.3.1 最接近特徵線25
2.3.2最接近特徵平面26
第三章線性鑑別式分析相關研究探討29
3.1 傳統線性鑑別式分析的基本原則29
3.2 改良式線性鑑別式分析33
第四章動態線性鑑別式分析37
4.1新增(Updating)39
4.1.1更新類別內散佈矩陣 39
4.1.2更新類別間散佈矩陣 40
4.2刪除(Downdating)42
4.2.1更新類別內散佈矩陣 42
4.2.2更新類別間散佈矩陣 43
4.3動態線性鑑別式分析演算法44
4.3.1動態更新平均值演算法46
4.3.2動態更新散佈矩陣演算法47
4.3.3時間複雜度分析48
第五章動態線性鑑別式分析於人臉驗證之應用49
5.1 假設檢定簡介49
5.2 貝氏因子於生物驗證上之應用50
5.3 最佳相似度估測與線性鑑別式分析之關係52
5.4 使用假設檢定於人臉驗證57
第六章實驗60
6.1 人臉資料庫介紹60
6.1.1 中研院人臉資料庫60
6.1.2 成功大學資訊工程學系汽車人臉資料庫61
6.2 動態更新線性鑑別式分析之轉換參數實驗62
6.2.1 新增資料庫中既有類別之影像資料62
6.2.2 刪除資料庫中既有類別之影像資料63
6.2.3 新增類別63
6.2.4 刪除類別63
6.3 人臉驗證實驗66
第七章Demo系統73
7.1 靜態人臉驗證展示73
7.2 動態人臉驗證展示75
第八章結論與未來展望79
8.1 結論79
8.2 未來研究方向80
參考文獻82
附錄一 中研院人臉資料庫部分人臉影像89
附錄二 成功大學資訊工程學系汽車人臉部分人臉影像91
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