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研究生：	曾富傑 Zen, Fu-Jie
論文名稱：	一使用小波之複雜背景單人人臉偵測方法 Wavelet-Based Single Face Detection under Complex Background
指導教授：	陳進興 Chen, Chin-Hsing
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 電機工程學系 Department of Electrical Engineering
論文出版年：	2004
畢業學年度：	92
語文別：	英文
論文頁數：	69
中文關鍵詞：	小波、人臉偵測、人臉
外文關鍵詞：	wavelet, face, face detection
相關次數：	點閱：94 下載：2
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　　人臉辨識是近年相當受到注目的一項研究，在很多應用上，如個人-電腦介面或是安全存取控制上皆被視為一相當重要的技術。人臉偵測則是人臉辨識的前置工作，能準確地定位出人臉，人臉辨識才能有高的辨識率。本論文是研究如何在一含有複雜背景的灰階影像中，精確地找出人臉所在。
　　本論文提出的人臉偵測方法分成兩個階段。階段一由分解後的小波係數取極值藉以框出初步的人臉。當階段一搜尋不到人臉時，再進行階段二對小波轉換後的影像進行二值化加強特徵，再一次偵測人臉。為提高後續的辨識率，框出人臉位置後再用積分投影方法找出眼睛所在，來修正人臉的位置。
　　本論文的實驗使用BioID、Visionic與自行拍攝的資料庫來做人臉偵測分析。BioID資料庫共有1521張影像，影像的大小是384×286，由23個不同的人以多種不同的姿勢、表情在不同的光線與背景條件下拍攝而成。Visionic資料庫共有112張影像，皆為不同的人所拍攝，影像大小不固定。自行拍攝的資料庫有62張，影像大小為512×384。有執行階段二的處理時間雖比沒執行階段二的略久，但差異不大，平均處理一張大小為384×286的影像所需時間皆約為1-2秒。沒有執行階段二與有執行階段二的平均偵測率分別為93%和96%。

　　Human face recognition has attracted many attentions in research in recent years. This technology is very important in many applications such as human-computer interfaces or security access control. Face detection is the leading phase of face recognition. Locating face correctly and accurately is necessary for high recognition-rate face recognition. This thesis deals with how to locate face correctly in gray-level images with complex backgrounds.
　　The proposed method in this thesis includes 2 stages. The stage 1 extracts extrema from wavelet coefficients of images after wavelet decomposition to locate face. When the stage 1 can not find the face, the stage 2 is included to locate face from the wavelet coefficients of images by thresholding. To improve the accuracy of face location, the method finds the eyes from the located face area by integral projection.
　　The experiments of this thesis use the BioID database, the Visionic database and the database shot by ourselves to test our proposed method. The BioID database consists 1521 images with a resolution of 384×286 pixels. These images show the frontal view of a face of one out of 23 different test persons and shot in some different conditions of expression, lighting, and background. The Visionic database consists of 112 pictures shot by 112 different people. The size of each picture is uncertain. There are 60 images in the databases shot by ourselves, the size of image is 512×384. The processing time of the method with stage 2 is longer than that of the method without stage 2, but the difference is small. The processing time for an image with a resolution of 384×286 pixels is one to two seconds. The average detection rate of the proposed method without stage 2 and with stage 2 are 93% and 96% respectively.

Abstract.......................................................................Ⅰ
Contents.......................................................................Ⅲ
Figure Captions................................................................Ⅴ 
Table Captions.................................................................Ⅹ

Chapter 1 Introduction..........................................................1
     1.1 Motivation.............................................................1 
     1.2 Related Research.......................................................2
     1.3 The Proposed Approach..................................................3
     1.4 Organization of the Thesis.............................................5
Chapter 2 Wavelet Transform.....................................................6
     2.1 Introduction...........................................................6
     2.2 Wavelet Transform in One Dimension.....................................6
     2.3 Wavelet Transform in Two Dimensions....................................8
Chapter 3 Face Detection.......................................................12
     3.1 Introduction..........................................................12
     3.2 Wavelet Transform.....................................................13
     3.3 The Stage 1...........................................................15
        3.3.1 Extrema in One Dimension.........................................15
        3.3.2 Extrema in Two Dimensions........................................17
        3.3.3 Threshold and Concentration......................................19
        3.3.4 Locating Face....................................................23
     3.4 The Stage 2...........................................................32
        3.4.1 Thresholding.....................................................32
        3.4.2 Locating Face....................................................34
Chapter 4 Eyes Detection.......................................................39
     4.1 Introduction..........................................................39
     4.2 Eyes Search by Using Integral Projection..............................40
        4.2.1 Integral Projection..............................................41
        4.2.2 Candidates Search................................................43
     4.3 Eyes Differentiation..................................................46
        4.3.1 Histogram Equalization...........................................47
        4.3.2 Face Pattern.....................................................49
     4.4 Location Refinement...................................................52
Chapter 5 Experiment Results and Discussion....................................55
     5.1 Introduction..........................................................55
     5.2 Experiment Result without Stage 2.....................................56
     5.3 Experiment Results with Stage 2.......................................59
     5.4 Experiments of Eyes Detection.........................................64
     5.5 Conclusion and Future Work............................................65
References.....................................................................67

                                    

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校內：2005-07-23公開
校外：2005-07-23公開

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