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Student Number	945202059
Author	Cheng-wei Lu(呂正偉)
Author's Email Address	No Public.
Statistics	This thesis had been viewed 925 times. Download 168 times.
Department	Computer Science and Information Engineering
Year	2007
Semester	2
Degree	Master
Type of Document	Master's Thesis
Language	English
Title	Face Feature Locating using Adaptive Active Shape Model
Date of Defense	2008-06-26
Page Count	76
Keyword	face detection face feature active shape model mean shape
Abstract	Recently, many face feature locating methods have been proposed. Active shape model has been shown to be a powerful tool to aid the interpretation of images, especially in face alignment. In this study, we propose a face facture location system using adaptive active shape model. The proposed system consists of two parts: (1) training process and (2) testing process. In the training process, we train a mean shape and transform matrix from training images. Then the testing process works by alternating the following steps: (i) Examine a region of image around each point for a better position. (ii) Update the shape parameters to fit the new found positions. In order to locate a better position for each point, we utilize the information of crisscross profiles around each point to decide the best position. We also utilize an adaptive affine transform to get a better reference position during testing process. In the experiments, the proposed approaches are evaluated by several different factors such as profiles, numbers of eigenvalues, and two kinds of affine transform. From the experiment results, we find that the proposed approaches can efficiently locate face feature and have better effect than the classical active shape models.
Table of Content	Abstract ....................................................................................................................... i Contents .................................................................................................................... ii List of Figures ............................................................................................................ v List of Tables .......................................................................................................... vii Chapter 1 Introduction ............................................................................................... 1 1.1 Motivation .................................................................................................... 1 1.2 System overview .......................................................................................... 1 1.3 Thesis organization ....................................................................................... 2 Chapter 2 Related Works ........................................................................................... 5 2.1 Face detection ............................................................................................... 5 2.1.1 Knowledge-based methods ................................................................. 7 2.1.2 Feature invariant ................................................................................. 7 2.1.3 Template matching ............................................................................ 11 2.1.4 Appearance-based methods .............................................................. 14 2.2 Active shape model ..................................................................................... 17 Chapter 3 Training process ...................................................................................... 18 3.1 Landmark points ......................................................................................... 19 3.2 Shapes ......................................................................................................... 20 3.3 Aligning shapes .......................................................................................... 25 3.4 Shape model and mean shape ..................................................................... 28 3.5 Active shape model .................................................................................... 29 Chapter 4 Testing process ........................................................................................ 31 4.1 Profile model .............................................................................................. 32 4.1.1 Forming a crisscross profile ............................................................. 32 4.1.2 Building the crisscross profile model during training ...................... 33 4.2 Searching for the best image shape ............................................................ 33 - iii - 4.3 Finding adaptive affine transformation ...................................................... 34 4.4 Calculating average error and maximum error .......................................... 34 Chapter 5 Experiments ............................................................................................ 37 5.1 Experimental platform ................................................................................ 37 5.2 Face databases ............................................................................................ 37 5.2.1 The IMM face database ..................................................................... 37 5.2.2 The MIT-CBCL face recognition database ....................................... 38 5.2.3 The Georgia Tech face database ....................................................... 39 5.2.4 The INDIAN face database ............................................................... 39 5.2.5 The GTAV face database ................................................................... 40 5.2.6 The IPVR face database .................................................................... 41 5.2.7 Driving images under various illuminations .................................... 42 5.3 Results of the proposed system .................................................................. 43 5.3.1 Results of the IMM face database ..................................................... 43 5.3.2 Results of the MIT-CBCL face recognition database ....................... 46 5.3.3 Results of the Georgia Tech face database ....................................... 49 5.3.4 Results of the INDIAN face database ............................................... 52 5.3.5 Results of the GTAV face database ................................................... 54 5.3.6 Results of the driving images under various illuminations .............. 57 5.4 Comparisons ............................................................................................... 59 5.4.1 Profile condition ............................................................................... 59 5.4.2 Affine transform ................................................................................ 66 5.4.3 Size of the eigenvalues ..................................................................... 69 Chapter 6 Conclusions ............................................................................................. 71 References ............................................................................................................... 72
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Advisor	Din-chang Tseng(曾定章)
Files	945202059.pdf approve in 1 year
Date of Submission	2008-07-16

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