Wu, King Keung. / "August 2011." / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 119-126). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview of Face Alignment Algorithms --- p.1 / Chapter 1.1.1 --- Objectives --- p.1 / Chapter 1.1.2 --- Motivation: Photo-realistic Talking Head --- p.2 / Chapter 1.1.3 --- Existing methods --- p.5 / Chapter 1.2 --- Contributions --- p.8 / Chapter 1.3 --- Outline of the Thesis --- p.11 / Chapter 2 --- Sparse Signal Representation --- p.13 / Chapter 2.1 --- Introduction --- p.13 / Chapter 2.2 --- Problem Formulation --- p.15 / Chapter 2.2.1 --- l0-nonn minimization --- p.15 / Chapter 2.2.2 --- Uniqueness --- p.16 / Chapter 2.3 --- Basis Pursuit --- p.18 / Chapter 2.3.1 --- From l0-norm to l1-norm --- p.19 / Chapter 2.3.2 --- l0-l1 Equivalence --- p.20 / Chapter 2.4 --- l1-Regularized Least Squares --- p.21 / Chapter 2.4.1 --- Noisy case --- p.22 / Chapter 2.4.2 --- Over-determined systems of linear equations --- p.22 / Chapter 2.5 --- Summary --- p.24 / Chapter 3 --- Sparse Corruptions and Principal Component Pursuit --- p.25 / Chapter 3.1 --- Introduction --- p.25 / Chapter 3.2 --- Sparse Corruptions --- p.26 / Chapter 3.2.1 --- Sparse Corruptions and l1-Error --- p.26 / Chapter 3.2.2 --- l1-Error and Least Absolute Deviations --- p.28 / Chapter 3.2.3 --- l1-Regularized l1-Error --- p.29 / Chapter 3.3 --- Robust Principal Component Analysis (RPCA) and Principal Component Pursuit --- p.31 / Chapter 3.3.1 --- Principal Component Analysis (PCA) and RPCA --- p.31 / Chapter 3.3.2 --- Principal Component Pursuit --- p.33 / Chapter 3.4 --- Experiments of Sparse and Low-rank Approach on Surveillance Video --- p.34 / Chapter 3.4.1 --- Least Squares --- p.35 / Chapter 3.4.2 --- l1-Regularized Least Squares --- p.35 / Chapter 3.4.3 --- l1-Error --- p.36 / Chapter 3.4.4 --- l1-Regularized l1-Error --- p.36 / Chapter 3.5 --- Summary --- p.37 / Chapter 4 --- Split Bregman Algorithm for l1-Problem --- p.45 / Chapter 4.1 --- Introduction --- p.45 / Chapter 4.2 --- Bregman Distance --- p.46 / Chapter 4.3 --- Bregman Iteration for Constrained Optimization --- p.47 / Chapter 4.4 --- Split Bregman Iteration for l1-Regularized Problem --- p.50 / Chapter 4.4.1 --- Formulation --- p.51 / Chapter 4.4.2 --- Advantages of Split Bregman Iteration . . --- p.52 / Chapter 4.5 --- Fast l1 Algorithms --- p.54 / Chapter 4.5.1 --- l1-Regularized Least Squares --- p.54 / Chapter 4.5.2 --- l1-Error --- p.55 / Chapter 4.5.3 --- l1-Regularized l1-Error --- p.57 / Chapter 4.6 --- Summary --- p.58 / Chapter 5 --- Face Alignment Using Sparse and Low-rank Decomposition --- p.61 / Chapter 5.1 --- Robust Alignment by Sparse and Low-rank Decomposition for Linearly Correlated Images (RASL) --- p.61 / Chapter 5.2 --- Problem Formulation --- p.62 / Chapter 5.2.1 --- Theory --- p.62 / Chapter 5.2.2 --- Algorithm --- p.64 / Chapter 5.3 --- Direct Extension of RASL: Multi-RASL --- p.66 / Chapter 5.3.1 --- Formulation --- p.66 / Chapter 5.3.2 --- Algorithm --- p.67 / Chapter 5.4 --- Matlab Implementation Details --- p.68 / Chapter 5.4.1 --- Preprocessing --- p.70 / Chapter 5.4.2 --- Transformation --- p.73 / Chapter 5.4.3 --- Jacobian Ji --- p.74 / Chapter 5.5 --- Experiments --- p.75 / Chapter 5.5.1 --- Qualitative Evaluations Using Small Dataset --- p.76 / Chapter 5.5.2 --- Large Dataset Test --- p.81 / Chapter 5.5.3 --- Conclusion --- p.85 / Chapter 5.6 --- Sensitivity analysis on selection of references --- p.87 / Chapter 5.6.1 --- References from consecutive frames --- p.88 / Chapter 5.6.2 --- References from RASL-aligned images --- p.91 / Chapter 5.7 --- Summary --- p.92 / Chapter 6 --- Extension of RASL for video: One-by-One Approach --- p.96 / Chapter 6.1 --- One-by-One Approach --- p.96 / Chapter 6.1.1 --- Motivation --- p.97 / Chapter 6.1.2 --- Algorithm --- p.97 / Chapter 6.2 --- Choices of Optimization --- p.101 / Chapter 6.2.1 --- l1-Regularized Least Squares --- p.101 / Chapter 6.2.2 --- l1-Error --- p.102 / Chapter 6.2.3 --- l1-Regularized l1-Error --- p.103 / Chapter 6.3 --- Experiments --- p.104 / Chapter 6.3.1 --- Evaluation for Different l1 Algorithms --- p.104 / Chapter 6.3.2 --- Conclusion --- p.108 / Chapter 6.4 --- Exploiting Property of Video --- p.109 / Chapter 6.5 --- Summary --- p.110 / Chapter 7 --- Conclusion and Future Work --- p.112 / Chapter A --- Appendix --- p.117 / Bibliography --- p.119

Thesis (Ph.D.)--University of Delaware, 2006. / Principal faculty advisor: Chandra Kambhamettu, Dept. of Computer & Information Sciences. Includes bibliographical references.

Thesis (Ph. D.)--University of Florida, 2001. / Title from first page of PDF file. Document formatted into pages; contains ix, 117 p.; also contains graphics. Vita. Includes bibliographical references (p. 109-116).

Thesis (Ph. D.)--Drexel University, 2006. / Includes abstract and vita. Includes bibliographical references (leaves 165-171).

The thesis illustrates the development of DSP-based¡§Real-Time Human Face Recognition System¡¨.The principal system consists of three major subsystems.There are Image Acquisition System¡AImage Preprocessing System and human face characteristic extraction . For experiment,we adopted colored face image with complex background and simulate on PC.We found the characteristic points and characteristic vectors from the face image which is searched from Gene algorithm.Then,we use the recognition system to recognize the face image.Finally we implant it to DSP. Shown by the experimental result,this system has good recognition and efficiency.

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 77-82)f.

Thesis (Ph.D.)--Memorial University of Newfoundland, 2001. / Bibliography: leaves 210-216.

Great progress has been made in face sketch synthesis in recent years. State-of-the-art methods commonly apply a Markov Random Fields (MRF) model to select local sketch patches from a set of training data. Such methods, however, have two major drawbacks. Firstly, the MRF model used cannot synthesize new sketch patches. Secondly, the optimization problem in solving the MRF is NP-hard. In this thesis, a novel Markov Weight Fields (MWF) model is proposed. By applying linear combination of candidate patches, MWF is capable of synthesizing new sketch patches. The MWF model can be formulated into a convex quadratic programming (QP) problem to which the optimal solution is guaranteed. Based on the Markov property of MWF model, a cascade decomposition method (CDM) is further proposed for solving such a large scale QP problem efficiently. Experiments show that the proposed CDM is very efficient, and only takes about 2:4 seconds. To deal with illumination changes of input photos, five special shading patches are included as candidate patches in addition to the patches selected from the training data. These patches help keeping structure of the face under different illumination conditions as well as synthesize shadows similar to the input photos. Extensive experiments on the CUHK face sketch database, AR database and Chinese celebrity photos show that the proposed model outperforms the common MRF model used in other state-of-the-art methods and is robust to illumination changes. / published_or_final_version / Computer Science / Master / Master of Philosophy

published_or_final_version / Computer Science and Information Systems / Master / Master of Philosophy

The main aim of this thesis is to generate 3D models of human heads from uncalibrated images. In order to extract geometric values of a human head, we find camera parameters using camera auto calibration. However, some image sequences generate non-unique (degenerate) solutions. An algorithm for removing degeneracy from the most common form of camera movement in face image acquisition is described. The geometric values of main facial features are computed initially. The model is then generated by gradual deformation of a generic polygonal model of a head. The accuracy of the models is evaluated using ground truth data from a range scanner. 3D models are covered with cylindrical texture values obtained from images. The models are appropriate for animation or identification applications.