Cheung, Kin Wang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 76-82). / Abstract also in Chinese. / Chapter 1. --- INTRODUCTION --- p.1 / Chapter 1.1. --- Background --- p.1 / Chapter 1.1.1. --- Introduction to Biometrics --- p.1 / Chapter 1.1.2. --- Face Recognition in General --- p.2 / Chapter 1.1.3. --- A Typical Face Recognition System Architecture --- p.4 / Chapter 1.1.4. --- Face Recognition in Surveillance Cameras --- p.6 / Chapter 1.1.5. --- Face recognition under Pose Variation --- p.9 / Chapter 1.2. --- Motivation and Objectives --- p.11 / Chapter 1.3. --- Related Works --- p.13 / Chapter 1.3.1. --- Overview of Pose-invariant Face Recognition --- p.13 / Chapter 1.3.2. --- Standard Face Recognition Setting --- p.14 / Chapter 1.3.3. --- Multi-Probe Setting --- p.19 / Chapter 1.3.4. --- Multi-Gallery Setting --- p.21 / Chapter 1.3.5. --- Non-frontal Face Databases --- p.23 / Chapter 1.3.6. --- Evaluation Metrics --- p.26 / Chapter 1.3.7. --- Summary of Non-frontal Face Recognition Settings --- p.27 / Chapter 1.4. --- Proposed Methods for Non-frontal Face Recognition --- p.28 / Chapter 1.5. --- Thesis Organization --- p.30 / Chapter 2. --- PARTIAL ELASTIC BUNCH GRAPH MATCHING --- p.31 / Chapter 2.1. --- Introduction --- p.31 / Chapter 2.2. --- EBGM for Non-frontal Face Recognition --- p.31 / Chapter 2.2.1. --- Overview of Baseline EBGM Algorithm --- p.31 / Chapter 2.2.2. --- Modified EBGM for Non-frontal Face Matching --- p.33 / Chapter 2.3. --- Experiments --- p.35 / Chapter 2.3.1. --- Experimental Setup --- p.35 / Chapter 2.3.2. --- Experimental Results --- p.37 / Chapter 2.4. --- Discussions --- p.40 / Chapter 3. --- FACE RECOGNITION BY FRONTAL VIEW SYNTHESIS WITH CALIBRATED STEREO CAMERAS --- p.43 / Chapter 3.1. --- Introduction --- p.43 / Chapter 3.2. --- Proposed Method --- p.44 / Chapter 3.2.1. --- Image Rectification --- p.45 / Chapter 3.2.2. --- Face Detection --- p.49 / Chapter 3.2.3. --- Head Pose Estimation --- p.51 / Chapter 3.2.4. --- Virtual View Generation --- p.52 / Chapter 3.2.5. --- Feature Localization --- p.54 / Chapter 3.2.6. --- Face Morphing --- p.56 / Chapter 3.3. --- Experiments --- p.58 / Chapter 3.3.1. --- Data Collection --- p.58 / Chapter 3.3.2. --- Synthesized Results --- p.59 / Chapter 3.3.3. --- Experiment Setup --- p.60 / Chapter 3.3.4. --- Experiment Results on FERET database --- p.61 / Chapter 3.3.5. --- Experiment Results on CAS-PEAL-R1 database --- p.62 / Chapter 3.4. --- Discussions --- p.64 / Chapter 3.5. --- Summary --- p.66 / Chapter 4. --- "EXPERIMENTS, RESULTS AND OBSERVATIONS" --- p.67 / Chapter 4.1. --- Experiment Setup --- p.67 / Chapter 4.2. --- Experiment Results --- p.69 / Chapter 4.3. --- Discussions --- p.70 / Chapter 5. --- CONCLUSIONS --- p.74 / Chapter 6. --- BIBLIOGRAPHY --- p.76

D.Ing. / This thesis deals with an investigation into facial recognition and some variables that influence the performance of such a system. Firstly there is an investigation into the influence of image variability on the overall recognition performance of a system and secondly the performance and subsequent suitability of a neural network based system is tested. Both tests are carried out on two distinctly different databases, one more variable than the other. The results indicate that the greater the amount of variability the more negatively affected is the performance rating of a specific facial recognition system. The results further indicate the success with the implementation of a neural network system over a more conventional statistical system.

Yuan Tianqiang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 51-55). / Abstracts in English and Chinese. / abstract --- p.i / acknowledgments --- p.iv / table of contents --- p.v / list of figures --- p.vii / list of tables --- p.ix / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Reflectional Symmetry Detection --- p.1 / Chapter 1.2 --- Research Progress on Face Analysis --- p.2 / Chapter 1.2.1 --- Face Detection --- p.3 / Chapter 1.2.2 --- Face Alignment --- p.4 / Chapter 1.2.3 --- Face Recognition --- p.6 / Chapter 1.3 --- Organization of this thesis --- p.8 / Chapter Chapter 2 --- Local reflectional symmetry detection --- p.9 / Chapter 2.1 --- Proposed Method --- p.9 / Chapter 2.1.1 --- Symmetry measurement operator --- p.9 / Chapter 2.1.2 --- Potential regions selection --- p.10 / Chapter 2.1.3 --- Detection of symmetry axes --- p.11 / Chapter 2.2 --- Experiments --- p.13 / Chapter 2.2.1 --- Parameter setting and analysis --- p.13 / Chapter 2.2.2 --- Experimental Results --- p.14 / Chapter Chapter 3 --- Global perspective reflectional symmetry detection --- p.16 / Chapter 3.1 --- Introduction of camera models --- p.16 / Chapter 3.2 --- Property of Symmetric Point-Pair --- p.18 / Chapter 3.3 --- analysis and Experiment --- p.20 / Chapter 3.3.1 --- Confirmative Experiments --- p.20 / Chapter 3.3.2 --- Face shape generation with PSI --- p.22 / Chapter 3.3.3 --- Error Analysis --- p.24 / Chapter 3.3.4 --- Experiments of Pose Estimation --- p.25 / Chapter 3.4 --- Summary --- p.28 / Chapter Chapter 4 --- Pre-processing of face analysis --- p.30 / Chapter 4.1 --- Introduction of Hough Transform --- p.30 / Chapter 4.2 --- Eye Detection --- p.31 / Chapter 4.2.1 --- Coarse Detection --- p.32 / Chapter 4.2.2 --- Refine the eyes positions --- p.34 / Chapter 4.2.3 --- Experiments and Analysis --- p.35 / Chapter 4.3 --- Face Components Detection with GHT --- p.37 / Chapter 4.3.1 --- Parameter Analyses --- p.38 / Chapter 4 3.2 --- R-table Construction --- p.38 / Chapter 4.3.3 --- Detection Procedure and Voting Strategy --- p.39 / Chapter 4.3.4 --- Experiments and Analysis --- p.41 / Chapter Chapter 5 --- Pose estimation with face symmetry --- p.45 / Chapter 5.1 --- Key points selection --- p.45 / Chapter 5.2 --- Face Pose Estimation --- p.46 / Chapter 5.2.1 --- Locating eye corners --- p.46 / Chapter 5.2.2 --- Analysis and Summary --- p.47 / Chapter Chapter 6 --- Conclusions and future work --- p.49 / bibliography --- p.51

The first approach, described in the first part of this thesis, studies 3D face modeling from multi-views. Today human face modeling and animation techniques are widely used to generate virtual characters and models. Such characters and models are used in movies, computer games, advertising, news broadcasting and other activities. We propose an efficient method to estimate the poses, the global shape and the local structures of a human head recorded in multiple face images or a video sequence by using a generic wireframe face model. Based on this newly proposed method, we have successfully developed a pose invariant face recognition system and a pose invariant face contour extraction method. / The objective of this thesis is to model and render complex scenes or objects from multiple images taken from different viewpoints. Two approaches to achieve this objective were investigated in this thesis. The first one is for known objects with prior geometrical models, which can be deformed to match the objects recorded in multiple input images. The second one is for general scenes or objects without prior geometrical models. / The proposed algorithms in this thesis were tested on many real and synthetic data. The experimental results illustrate their efficiency and limitations. / The second approach, described in the second part of this thesis, investigates 3D modeling and rendering for general complex scenes. The entertainment industry touches hundreds of millions of people every day, and synthetic pictures and 3D reconstruction of real scenes, often mixed with actual film footage, are now common place in computer games, sports broadcasting, TV advertising and feature films. A series of techniques has been developed to complete this task. First, a new view-ordering algorithm was proposed to organize and order an unorganized image database. Second, a novel and efficient multiview feature matching approach was developed to calibrate and track all views. Finally, both match propagation based and Bayesian based methods were developed to produce 3D scene models for rendering. / Yao Jian. / "September 2006." / Adviser: Wai-Kuen Chan. / Source: Dissertation Abstracts International, Volume: 68-03, Section: B, page: 1849. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (p. 170-181). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Facial expression recognition has become an active research topic in recent years due to its applications in human computer interfaces and data-driven animation. In this thesis, we focus on the problem of how to e?ectively use domain, temporal and categorical information of facial expressions to help computer understand human emotions. Over the past decades, many techniques (such as neural networks, Gaussian processes, support vector machines, etc.) have been applied to facial expression analysis. Recently graphical models have emerged as a general framework for applying probabilistic models. They provide a natural framework for describing the generative process of facial expressions. However, these models often su?er from too many latent variables or too complex model structures, which makes learning and inference di±cult. In this thesis, we will try to analyze the deformation of facial expression by introducing some recently developed graphical models (e.g. latent topic model) or improving the recognition ability of some already widely used models (e.g. HMM). In this thesis, we develop three di?erent graphical models with di?erent representational assumptions: categories being represented by prototypes, sets of exemplars and topics in between. Our ¯rst model incorporates exemplar-based representation into graphical models. To further improve computational e±- ciency of the proposed model, we build it in a local linear subspace constructed by principal component analysis. The second model is an extension of the recently developed topic model by introducing temporal and categorical information into Latent Dirichlet Allocation model. In our discriminative temporal topic model (DTTM), temporal information is integrated by placing an asymmetric Dirichlet prior over document-topic distributions. The discriminative ability is improved by a supervised term weighting scheme. We describe the resulting DTTM in detail and show how it can be applied to facial expression recognition. Our third model is a nonparametric discriminative variation of HMM. HMM can be viewed as a prototype model, and transition parameters act as the prototype for one category. To increase the discrimination ability of HMM at both class level and state level, we introduce linear interpolation with maximum entropy (LIME) and member- ship coe±cients to HMM. Furthermore, we present a general formula for output probability estimation, which provides a way to develop new HMM. Experimental results show that the performance of some existing HMMs can be improved by integrating the proposed nonparametric kernel method and parameters adaption formula. In conclusion, this thesis develops three di?erent graphical models by (i) combining exemplar-based model with graphical models, (ii) introducing temporal and categorical information into Latent Dirichlet Allocation (LDA) topic model, and (iii) increasing the discrimination ability of HMM at both hidden state level and class level. / published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

Tang Yuk Ming = 電影中三維人面模型之估計 / 鄧育明. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 107-113). / Text in English; abstracts in English and Chinese. / Tang Yuk Ming = Dian ying zhong san wei ren mian mo xing zhi gu ji / Deng Yuming. / Acknowledgement --- p.i / Abstract --- p.ii / Contents --- p.vi / List of Figures --- p.viii / List of Tables --- p.x / List of Abbreviations and Notations --- p.xi / Chapter 1. --- Introduction --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- Recent Research Works --- p.2 / Chapter 1.2.1 --- Face modeling from images --- p.2 / Chapter 1.2.2 --- Pose estimation --- p.4 / Chapter 1.3 --- Objectives and Assumptions --- p.7 / Chapter 1.4 --- Our Method --- p.8 / Chapter 1.5 --- Thesis Outline --- p.10 / Chapter 2. --- Basic Theory on 3D Modeling of a Head --- p.11 / Chapter 2.1 --- Introduction --- p.11 / Chapter 2.2 --- Perspective Projection --- p.13 / Chapter 2.3 --- Initialization --- p.17 / Chapter 2.3.1 --- Generic wireframe face model and fiducial points --- p.17 / Chapter 2.3.2 --- Deformations --- p.22 / Chapter 2.3.3 --- Experimental results --- p.35 / Chapter 2.4 --- Summary --- p.39 / Chapter 3. --- Pose Estimation --- p.40 / Chapter 3.1 --- Introduction --- p.40 / Chapter 3.2 --- Problem Description --- p.42 / Chapter 3.3 --- Iterative Least-Square Minimization --- p.45 / Chapter 3.3.1 --- Depth estimation --- p.45 / Chapter 3.3.2 --- Least-square minimization --- p.47 / Chapter 3.3.3 --- Iterative process --- p.52 / Chapter 3.4 --- Experimental Results --- p.54 / Chapter 3.4.1 --- Synthetic data --- p.54 / Chapter 3.4.2 --- Real data --- p.65 / Chapter 3.5 --- Summary --- p.69 / Chapter 4. --- 3D Wireframe Model Estimation --- p.70 / Chapter 4.1 --- Introduction --- p.70 / Chapter 4.2 --- 3D Wireframe Model Estimation --- p.72 / Chapter 4.2.1 --- Least-square minimization --- p.73 / Chapter 4.2.2 --- Iterative process --- p.74 / Chapter 4.3 --- 3D Wireframe Model Estimation of the Subsequent Frames --- p.77 / Chapter 4.4 --- Experimental Results --- p.78 / Chapter 4.4.1 --- Synthetic data --- p.78 / Chapter 4.4.2 --- Real data --- p.84 / Chapter 4.5 --- Summary --- p.98 / Chapter 5. --- Contributions and Conclusions --- p.99 / Chapter 5.1 --- Contributions and conclusions --- p.99 / Chapter 5.2 --- Future Developments --- p.102 / Appendix A Triangles and vertices on the IST model --- p.104 / Bibliography --- p.107