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研究生:林雨蓉
研究生(外文):LIN, YU-JUNG
論文名稱:基於影像品質分析之生物辨識詐騙偵測
論文名稱(外文):Biometric Spoofing Detection Based On Image Quality Analysis
指導教授:蔡耀弘蔡耀弘引用關係
指導教授(外文):TSAI, YAO-HONG
口試委員:楊權輝鄭瑞恒蔡耀弘
口試委員(外文):YANG, CHYUAN-HUEICHENG, REI-HENGTSAI, YAO-HONG
口試日期:2017-07-27
學位類別:碩士
校院名稱:玄奘大學
系所名稱:資訊管理學系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2017
畢業學年度:106
語文別:中文
論文頁數:30
中文關鍵詞:人臉辨識欺騙偵測局部二值碼支持向量機
外文關鍵詞:BiometricsFace RecognitionSpoofing DetectionLocal Binary PatternsSupport Vector Machines
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現今社會中,人臉辨識系統的技術不斷推陳出新,應用的範疇也愈來愈廣泛,從安全監控系統、居家門禁系統、課堂點名系統至手機解鎖等,均可見人臉辨識系統的應用,由於人臉辨識系統是使用臉部紋理特徵及輪廓進行判別,容易被有心人士使用數位影像、視訊或列印圖片等偽造方式欺騙辨識系統。為解決人臉辨識容易遭受偽造攻擊的問題,在本研究中我們提出以下辦法來解決容易遭受偽造攻擊的問題。首先,我們針對人臉拍攝兩張焦距不同的影像,若為真實人臉,在不同焦距中,會產生局部模糊與局部清楚的現象;相對地,如果使用手機、平板等播放數位影像或視訊進行偽造攻擊時,在進行不同焦距拍攝情境下,只會獲得全部模糊或全部清楚的影像,並且會產生干擾的雜訊,我們利用局部二值碼(Local Binary Patterns, LBP)計算連續拍攝的兩張照片,分析影像紋理,同時,透過支持向量機(Support Vector Machines, SVM)做人臉的判別分析,可以達到偵測詐騙攻擊的目的。
In modern societies, the application of face recognition system is more and more widely from the surveillance system, household security system to classroom check-in system, etc. Since the face recognition system is usually based on texture features and contours of the face, the intruder may use a photo, video, or image, to deceive the recognition system, i.e. spoofing. In order to perform spoofing detection, we propose the following solution. First we capture two face images through different focus positions. For real face, we produce partial clear and partial blurred face image in different focus positions. For spoofing by phone or tablet, it will relatively result in all blurred or all clear face images on the same focus positions for capturing real face. In addition, it will also have Moire pattern for capturing images from display devices. Next we compute the
Local Binary Patterns (LBP) of the two pair of images. The LBP for the pair of real face will be greater than that from the phone or tablet. In our experiments, five testing sets were performed to verify the effectiveness of the proposed method to the problem.

摘要 i
Abstract ii
致謝 iv
圖目錄 vii
第一章 前言 1
1.1 研究背景 1
1.2研究目的 2
1.3論文架構 3
第二章 文獻回顧 4
第三章 研究方法 8
3.1影像灰階處理 9
3.2人臉偵測 9
3.3人臉區域擷取 13
3.4特徵提取 13
3.5特徵向量直方圖 16
3.6兩張影像特徵向量直方圖差值 16
3.7支持向量機(Support Vector Machines, SVM) 17
第四章 實驗結果 21
4.1資料庫說明 21
4.2作業平台與取像設備 24
4.3實驗結果 25
第五章 結論 26
第六章 參考文獻 27

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