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研究生: 張偉杰
論文名稱: 非使用者配合環境下之人臉辨識研究
Face Recognition in Non-Cooperative User Environment
指導教授: 李忠謀
學位類別: 碩士
Master
系所名稱: 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2010
畢業學年度: 98
語文別: 中文
論文頁數: 53
中文關鍵詞: 人臉辨識使用者配合環境非使用者配合環境
英文關鍵詞: Face Recognition, Cooperative User Environment, Non-Cooperative User Environment
論文種類: 學術論文
相關次數: 點閱:97下載:7
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  • 人臉辨識是近年來很熱門的研究,希望透過人臉辨識來達到身份的識別,而省去以往在不同的服務及場合下需要配戴很多張不同的證件。為此發展出來之人臉辨識,我們稱它為「使用者配合環境」,是在想要索取服務的情況下,進而配合攝影機來執行人臉辨識;反之在監視系統中,有人自然的從走廊經過,我們想透過走廊內的攝影機知道他是誰,在過程中該人並未配合攝影機來執行人臉辨識,所以像這類的情況我們稱它為「非使用者配合環境」,在本論文中將討論兩者之差異。文中我們透過在階梯教室裡不經由學生配合的情況下來執行人臉辨識,藉此來討論人臉辨識如何實現在非使用者配合環境下。

    The researches of face recognition have been more and more popular in recent years. In different services and occasions, we need a lot of certificates to provide identity. By using the face recognition, we could save the paperwork. Therefore, the face recognition is developed by the above-mentioned that we call "Cooperative User Environment". It's a service which coordinates with the camera to implement the face recognition. On the other hand, we would like to know the person who passes through the corridor in the surveillance system. In this process, the person doesn't coordinate with the camera to implement the face recognition that we call "Non-Cooperative User Environment". In this paper, we will discuss the differences between cooperative user environment and non-cooperative user environment. We use the face recognition to make a roll call in the ladder classroom without the students' cooperation. In the experiments, we could know how the face recognition is implemented in the non-cooperative user environment.

    目錄 目錄 I 附圖目錄 III 附表目錄 IV 第一章 緒論 1 1.1 研究動機 1 1.2 研究目的 3 1.3 研究範圍與限制 4 1.4 論文架構 5 第二章 文獻探討 6 2.1 以線性分析法進行人臉辨識 8 2.1.1 特徵臉(EigenFaces) 8 2.1.2 費雪臉(FisherFaces) 8 2.1.3 獨立成份分析法(ICA) 9 2.1.4 保留局部關係之投影(LPP) 9 2.2 以擷取之特徵進行人臉辨識 11 2.2.1 線段邊緣圖(Line Edge Map) 11 2.2.2 局部二元圖形(Local Binary Pattern) 11 第三章 二維線性鑑別分析法 13 第四章 方法架構與流程 18 4.1 影像來源與前處理 19 4.1.1 個人之人臉影像 19 4.1.2 多人之人臉影像 20 4.1.3 人臉偵測 21 4.1.4 人臉影像前處理 22 4.2 二維線性鑑別分析法之訓練階段 23 4.3 二維線性鑑別分析法之測試階段 24 4.4 相互參考信任機制 25 4.4.1 參考最小距離(Minimal Distance) 27 4.4.2 參考最大偏差(Maximal Deviation) 28 第五章 實驗結果與分析 31 5.1 iRCDB資料庫 31 5.2 本研究使用iRCDB資料庫之實驗 32 5.2.1 實驗流程 33 5.2.2 不同數量的訓練資料對辨識結果之影響 37 5.2.3 相互參考機制對單一時間點多人人臉辨識之影響 39 5.2.4 相互參考機制用於其它資料庫之實驗 40 5.3 實驗結論與分析 41 第六章 結論與未來展望 43 參考文獻 45 附錄 48 附錄一First分類器未使用直方圖等化於不同前處理方式之辨識率 48 附錄二First分類器使用直方圖等化於不同前處理方式之辨識率 49 附錄三Second分類器未使用直方圖等化於不同前處理方式之辨識率 50 附錄四Second分類器使用直方圖等化於不同前處理方式之辨識率 51 附錄五Mix分類器未使用直方圖等化於不同前處理方式之辨識率 52 附錄六Mix分類器未使用直方圖等化於不同前處理方式之辨識率 53   附圖目錄 圖1.1 將人臉辨識技術應用在教室門禁管制(左)與海關旅行證件識別(右)[15] 2 圖1.2 人臉的變化(外觀、表情) [8] 2 圖1.3 人臉在光線變化下的影響(每列為同個人)[15] 4 圖2.1 取前五大特徵根建立之特徵臉(EigenFaces)[12] 8 圖2.2 取前五大特徵根所建立之費雪臉(FisherFaces)[12] 9 圖2.3人臉之邊緣特徵圖(左)進行多邊線段擬合(右)[8] 10 圖2.4 局部二元圖形(LBP)之特徵擷取方法[10] 11 圖3.1 2DLDA示意圖[26] 14 圖4.1流程架構圖 18 圖4.2個人之人臉影像拍攝情況(左)以及多人之人臉影像拍攝情況(右) 19 圖4.3個人之人臉影像經人臉偵測擷取後的範例 20 圖4.4多人之人臉影像經人臉偵測擷取後的範例 21 圖4.5人臉影像前處理示意圖 22 圖4.6二維線性鑑別分析法之子空間投影示意圖 23 圖4.7二維線性鑑別分析法之子空間投影後各類別中心點示意圖 24 圖4.8人臉影像投影至子空間後之分類示意圖 25 圖4.9分類器無法有效分類之狀況示意圖 30 圖5.1 First分類器未使用直方圖等化於不同前處理方式之曲線圖 34 圖5.2 First分類器使用直方圖等化於不同前處理方式之曲線圖 34 圖5.3 Second分類器未使用直方圖等化於不同前處理方式之曲線圖 35 圖5.4 Second分類器使用直方圖等化於不同前處理方式之曲線圖 35 圖5.5 Mix分類器未使用直方圖等化於不同前處理方式之曲線圖 36 圖5.6 Mix分類器使用直方圖等化於不同前處理方式之曲線圖 36 圖5.7所有同一時間點內的所有人臉示意圖 39 圖5.8實際於階梯教室內課堂之情況圖 40 圖5.9不同時間點之間人臉影像的差異 42   附表目錄 表4.1單人人臉辨識結果示意表 26 表4.2多人人臉辨識相互參考最小距離之結果示意表 27 表4.3 由距離轉換為偏差值之示意表 28 表4.4多人人臉辨識相互參考最大偏差之結果示意表 29 表5.1不同數量的訓練資料對辨識結果之影響 38 表5.2相互參考機制對單一時間點的多人人臉辨識的比較 40 表5.3相互參考機制用於AT&T資料庫之辨識結果 41

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