Sin Ka Yu = 抽取臉孔特徵及辨認臉孔的統計學方法 / 冼家裕. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 86-90). / Text in English; abstracts in English and Chinese. / Sin Ka Yu = Chou qu lian kong te zheng ji bian ren lian kong de tong ji xue fang fa / Xian Jiayu. / Chapter Chapter 1. --- Introduction --- p.1 / Chapter 1.1. --- Motivation --- p.1 / Chapter 1.2. --- Objectives --- p.4 / Chapter 1.3. --- Organization of the thesis --- p.4 / Chapter Chapter 2. --- Facial Feature Extraction --- p.6 / Chapter 2.1. --- Introduction --- p.6 / Chapter 2.2. --- Reviews of Statistical Approach --- p.8 / Chapter 2.2.1. --- Eigenfaces --- p.8 / Chapter 2.2.1.1. --- Eigenfeatures Error! Bookmark not defined / Chapter 2.2.3. --- Singular Value Decomposition --- p.14 / Chapter 2.2.4. --- Summary --- p.15 / Chapter 2.3. --- Review of fiducial point localization methods --- p.16 / Chapter 2.3.1. --- Symmetry based Approach --- p.16 / Chapter 2.3.2. --- Color Based Approaches --- p.17 / Chapter 2.3.3. --- Integral Projection --- p.17 / Chapter 2.3.4. --- Deformable Template --- p.20 / Chapter 2.4. --- Corner-based Fiducial Point Localization --- p.22 / Chapter 2.4.1. --- Facial Region Extraction --- p.22 / Chapter 2.4.2. --- Corner Detection --- p.25 / Chapter 2.4.3. --- Corner Selection --- p.27 / Chapter 2.4.3.1. --- Mouth Corner Pairs Detection --- p.27 / Chapter 2.4.3.2. --- Iris Detection --- p.27 / Chapter 2.5. --- Experimental Results --- p.30 / Chapter 2.6. --- Conclusions --- p.30 / Chapter 2.7. --- Notes on Publications --- p.30 / Chapter Chapter 3. --- Fiducial Point Extraction with Shape Constraint --- p.32 / Chapter 3.1. --- Introduction --- p.32 / Chapter 3.2. --- Statistical Theory of Shape --- p.33 / Chapter 3.2.1. --- Shape Space --- p.33 / Chapter 3.2.2. --- Shape Distribution --- p.34 / Chapter 3.3. --- Shape Guided Fiducial Point Localization --- p.38 / Chapter 3.3.1. --- Shape Constraints --- p.38 / Chapter 3.3.2. --- Intelligent Search --- p.40 / Chapter 3.4. --- Experimental Results --- p.40 / Chapter 3.5. --- Conclusions --- p.42 / Chapter 3.6. --- Notes on Publications --- p.42 / Chapter Chapter 4. --- Statistical Pattern Recognition --- p.43 / Chapter 4.1. --- Introduction --- p.43 / Chapter 4.2. --- Bayes Decision Rule --- p.44 / Chapter 4.3. --- Gaussian Maximum Probability Classifier --- p.46 / Chapter 4.4. --- Maximum Likelihood Estimation of Mean and Covariance Matrix --- p.48 / Chapter 4.5. --- Small Sample Size Problem --- p.50 / Chapter 4.5.1. --- Dispersed Eigenvalues --- p.50 / Chapter 4.5.2. --- Distorted Classification Rule --- p.55 / Chapter 4.6. --- Review of Methods Handling the Small Sample Size Problem --- p.57 / Chapter 4.6.1. --- Linear Discriminant Classifier --- p.57 / Chapter 4.6.2. --- Regularized Discriminant Analysis --- p.59 / Chapter 4.6.3. --- Leave-one-out Likelihood Method --- p.63 / Chapter 4.6.4. --- Bayesian Leave-one-out Likelihood method --- p.65 / Chapter 4.7. --- Proposed Method --- p.68 / Chapter 4.7.1. --- A New Covariance Estimator --- p.70 / Chapter 4.7.2. --- Model Selection --- p.75 / Chapter 4.7.3. --- The Mixture Parameter --- p.76 / Chapter 4.8. --- Experimental results --- p.77 / Chapter 4.8.1. --- Implementation --- p.77 / Chapter 4.8.2. --- Results --- p.79 / Chapter 4.9. --- Conclusion --- p.81 / Chapter 4.10. --- Notes on Publications --- p.82 / Chapter Chapter 5. --- Conclusions and Future works --- p.83 / Chapter 5.1. --- Conclusions and Contributions --- p.83 / Chapter 5.2. --- Future Works --- p.84
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_324649 |
| Date | January 2004 |
| Contributors | Sin, Ka Yu., Chinese University of Hong Kong Graduate School. Division of Electronic Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | print, 91 leaves : ill. ; 30 cm. |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
Page generated in 0.0042 seconds