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研究生:陳怡珊
研究生(外文):I-Shan Chen
論文名稱:使用適應性機率模型之多使用者辨識系統
論文名稱(外文):Multi-client identification system using adaptive probabilistic model (APM)
指導教授:林進燈林進燈引用關係
指導教授(外文):Chin-Teng Lin
學位類別:碩士
校院名稱:國立交通大學
系所名稱:資訊學院資訊科技產業專班
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:2008
畢業學年度:96
語文別:英文
論文頁數:55
中文關鍵詞:人臉偵測人臉辨識
外文關鍵詞:face detectionface identificationface recognition
相關次數:
  • 被引用被引用:0
  • 點閱點閱:195
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  • 下載下載:21
  • 收藏至我的研究室書目清單書目收藏:0
本論文提出一個快速而且實用性高的人臉識別系統,其用途在於偵測影像中的人臉位置並辨識該人臉是否為使用者。本系統包含人臉偵測(face detection),人臉識別(face identification)等。由於此系統以實作在即時系統上為前提來進行研究,及時處理(real-time)的要求極為嚴苛,計算量及精準度成為本論文的第一要求。系統的第一部份在偵測影像中人臉的位置,利用直方圖匹配的方法來對人臉資料庫做亮度上的正規化,接著使用2D haar 為特徵以AdaBoost 學習演算法並加以串聯式架構的觀念來進行人臉偵測器的訓練,同時提出以區域為基礎的分群法則來作為後端處理。而系統的第二部份在於辨識該人臉為使用者或入侵者,首先使用主成分分析方法,以找出重要的資訊及減少資料量的目的下來擷取人臉特徵,接著利用本論文提出的一個適應性機率模型(APM)來進行多個人臉上的識別,在APM 的設計上允許線上新增使用者以及同步更新使用者資訊。藉由以上提出的系
統,我們可以在複雜的環境下,進行多個人臉的偵測及識別,讓系統
在執行環境下更有彈性跟實用性。
This thesis tends to accomplish a fast and practical face recognition system. The target of this system is to locate face regions from a captured image and distinguish if these faces belong to any registered client. A multi-client system using adaptive probabilistic model (APM) is proposed
to achieve a complete face recognition system which consists of the face detection unit and the face identification unit. In the face detection unit, an AdaBoost-based detector is implemented and improved by a lighting normalization technique and a region-based clustering method. In the face identification, the adaptive probabilistic model (APM) is proposed to establish a simple model for each registered client. Due to the design of
APM, the proposed system can on-line add new clients and update the information of clients. Furthermore, the practicability and performance of the proposed system are demonstrated in the experimental results in this thesis.
Chapter 1 Introduction ................................... 1
1.1 Motivation .......................................... 1
1.2 Related work ........................................ 2
1.3 Thesis organization ................................. 5
1.4 System architecture ................................. 5
Chapter 2 Face Detection ................................. 7
2.1 Lighting normalization .............................. 9
2.2 Features ........................................... 12
2.3 Training of detector ............................... 15
2.4 Post-process: A region-based clustering method ..... 19
Chapter 3 Face Identification ........................... 22
3.1 Features ........................................... 23
3.2 Adaptive probabilistic model (APM) ................. 26
3.2.1 Similary measure ................................. 27
3.2.2 Parameter tuning ................................. 29
3.2.3 Adaptive updating .................................30
Chapter 4 Experimental Results .......................... 33
4.1 Face detection ..................................... 33
4.2 Face identification ................................ 38
4.2.1 Off-line testing ................................. 38
4.2.2 On-line testing .................................. 43
4.3 Discussion ......................................... 48
Chapter 5 Conclusions and Future Work ....................51
References ...............................................53
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