Zhang Wei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 73-78). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.ii / List of Figures --- p.viii / List of Tables --- p.ix / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Previous work --- p.2 / Chapter 1.1.1 --- Learning-based approaches --- p.3 / Chapter 1.1.2 --- Feature-based approaches --- p.7 / Chapter 1.2 --- Thesis objective --- p.12 / Chapter 1.3 --- The proposed detector --- p.13 / Chapter 1.4 --- Thesis outline --- p.14 / Chapter 2 --- The Edge Merging Algorithm --- p.16 / Chapter 2.1 --- Edge detection --- p.16 / Chapter 2.2 --- Edge breaking --- p.18 / Chapter 2.2.1 --- Cross detection --- p.20 / Chapter 2.2.2 --- Corner detection --- p.20 / Chapter 2.3 --- Curve merging --- p.23 / Chapter 2.3.1 --- The search region --- p.25 / Chapter 2.3.2 --- The merging cost function --- p.27 / Chapter 2.4 --- Ellipse fitting --- p.30 / Chapter 2.5 --- Discussion --- p.33 / Chapter 3 --- The Face Verifier --- p.35 / Chapter 3.1 --- The face box --- p.35 / Chapter 3.1.1 --- Face box localization --- p.36 / Chapter 3.1.2 --- Conditioning the face box --- p.42 / Chapter 3.2 --- Eye-mouth triangle search --- p.45 / Chapter 3.3 --- Face model matching --- p.48 / Chapter 3.3.1 --- Face model construction --- p.48 / Chapter 3.3.2 --- Confidence of detection --- p.51 / Chapter 3.4 --- Dealing with overlapped detections --- p.51 / Chapter 3.5 --- Discussion --- p.53 / Chapter 4 --- Experiments --- p.55 / Chapter 4.1 --- The test sets --- p.55 / Chapter 4.2 --- Experimental results --- p.56 / Chapter 4.2.1 --- The ROC curves --- p.56 / Chapter 4.3 --- Discussions --- p.61 / Chapter 5 --- Conclusions --- p.69 / Chapter 5.1 --- Conclusions --- p.69 / Chapter 5.2 --- Suggestions for future work --- p.70 / List of Original Contributions --- p.72 / Bibliography --- p.73

Pun Kwok Ho. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 106-111). / Abstracts in English and 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 --- Typical Face Recognition Systems --- p.4 / Chapter 1.1.4 --- Face Database and Evaluation Protocol --- p.5 / Chapter 1.1.5 --- Evaluation Metrics --- p.7 / Chapter 1.1.6 --- Characteristics of Mobile Devices --- p.10 / Chapter 1.2 --- Motivation and Objectives --- p.12 / Chapter 1.3 --- Major Contributions --- p.13 / Chapter 1.3.1 --- Optimization Framework --- p.13 / Chapter 1.3.2 --- Real Time Principal Component Analysis --- p.14 / Chapter 1.3.3 --- Real Time Elastic Bunch Graph Matching --- p.14 / Chapter 1.4 --- Thesis Organization --- p.15 / Chapter 2. --- Related Work --- p.16 / Chapter 2.1 --- Face Recognition for Desktop Computers --- p.16 / Chapter 2.1.1 --- Global Feature Based Systems --- p.16 / Chapter 2.1.2 --- Local Feature Based Systems --- p.18 / Chapter 2.1.3 --- Commercial Systems --- p.20 / Chapter 2.2 --- Biometrics on Mobile Devices --- p.22 / Chapter 3. --- Optimization Framework --- p.24 / Chapter 3.1 --- Introduction --- p.24 / Chapter 3.2 --- Levels of Optimization --- p.25 / Chapter 3.2.1 --- Algorithm Level --- p.25 / Chapter 3.2.2 --- Code Level --- p.26 / Chapter 3.2.3 --- Instruction Level --- p.27 / Chapter 3.2.4 --- Architecture Level --- p.28 / Chapter 3.3 --- General Optimization Workflow --- p.29 / Chapter 3.4 --- Summary --- p.31 / Chapter 4. --- Real Time Principal Component Analysis --- p.32 / Chapter 4.1 --- Introduction --- p.32 / Chapter 4.2 --- System Overview --- p.33 / Chapter 4.2.1 --- Image Preprocessing --- p.33 / Chapter 4.2.2 --- PCA Subspace Training --- p.34 / Chapter 4.2.3 --- PCA Subspace Projection --- p.36 / Chapter 4.2.4 --- Template Matching --- p.36 / Chapter 4.3 --- Optimization using Fixed-point Arithmetic --- p.37 / Chapter 4.3.1 --- Profiling Analysis --- p.37 / Chapter 4.3.2 --- Fixed-point Representation --- p.38 / Chapter 4.3.3 --- Range Estimation --- p.39 / Chapter 4.3.4 --- Code Conversion --- p.42 / Chapter 4.4 --- Experiments and Discussions --- p.43 / Chapter 4.4.1 --- Experiment Setup --- p.43 / Chapter 4.4.2 --- Execution Time --- p.44 / Chapter 4.4.3 --- Space Requirement --- p.45 / Chapter 4.4.4 --- Verification Accuracy --- p.45 / Chapter 5. --- Real Time Elastic Bunch Graph Matching --- p.49 / Chapter 5.1 --- Introduction --- p.49 / Chapter 5.2 --- System Overview --- p.50 / Chapter 5.2.1 --- Image Preprocessing --- p.50 / Chapter 5.2.2 --- Landmark Localization --- p.51 / Chapter 5.2.3 --- Feature Extraction --- p.52 / Chapter 5.2.4 --- Template Matching --- p.53 / Chapter 5.3 --- Optimization Overview --- p.54 / Chapter 5.3.1 --- Computation Optimization --- p.55 / Chapter 5.3.2 --- Memory Optimization --- p.56 / Chapter 5.4 --- Optimization Strategies --- p.58 / Chapter 5.4.1 --- Fixed-point Arithmetic --- p.60 / Chapter 5.4.2 --- Gabor Masks and Bunch Graphs Precomputation --- p.66 / Chapter 5.4.3 --- Improving Array Access Efficiency using ID array --- p.68 / Chapter 5.4.4 --- Efficient Gabor Filter Selection --- p.75 / Chapter 5.4.5 --- Fine Tuning System Cache Policy --- p.79 / Chapter 5.4.6 --- Reducing Redundant Memory Access by Loop Merging --- p.80 / Chapter 5.4.7 --- Maximizing Cache Reuse by Array Merging --- p.90 / Chapter 5.4.8 --- Optimization of Trigonometric Functions using Table Lookup. --- p.97 / Chapter 5.5 --- Summary --- p.99 / Chapter 6. --- Conclusions --- p.103 / Chapter 7. --- Bibliography --- p.106

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

Finally, a performance driven face animation system is introduced. The proposed system consists of two key components: a robust non-rigid 3D tracking module and a MPEG4 compliant facial animation module. Firstly, the facial motion is tracked from source videos which contain both the rigid 3D head motion (6 DOF) and the non-rigid expression variation. Afterward, the tracked facial motion is parameterized via estimating a set of MPEG4 facial animation parameters (FAP) and applied to drive the animation of the target avatar. / In the first part of the thesis, the problem of tracking a non-rigid 3D face is studied. A novel framework for non-rigid 3D face tracking is proposed for applications in live scenarios. In order to extract more information of feature correspondences, the proposed framework integrates three types of features which discriminate face deformation across different views. The integration of these complementary features is important for robust estimation of the 3D parameters. In order to estimate the high dimensional 3D deformation parameters, we develop a hierarchical parameter estimation algorithm to robustly estimate both rigid and non-rigid 3D parameters. We show the importance of both features fusion and hierarchical parameter estimation for reliable tracking 3D face deformation. Experiments demonstrate the robustness and accuracy of the proposed algorithm especially in the cases of agile head motion, drastic illumination change, and large pose change up to profile view. / The video based face recognition is studied in the second part of the thesis. Compared to the still image based recognition methods, the video based methods share the merits of spatial temporal coherence among image sequences and overcomplete training samples. We propose a framework for the task of face recognition in real-world noisy videos based on 3D deformable face tracking, which can directly estimate face pose for a view-based face recognition scheme. Meanwhile, the precise non-rigid tracking provides well-aligned face samples for the subsequent recognizer. At the recognition stage, three types of feature descriptors, including Regularized LDA, LE and sparse representation, are exploited. Extensive experiments conducted on the real world videos demonstrate that the proposed recognition framework can achieve the state-of-the art recognition results, even with the usage of a simple classifier. / Three dimensional face tracking is a crucial task for many applications in computer vision. Problem like face recognition, facial expression analysis and animation, are more likely to be solved by if the geometry and appearance properties are available through a 3D face tracker. / Zhang, Wei. / Adviser: Xiaoon Tang. / Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 102-113). / 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 Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Orientador: Anna Diva Plasencia Lotufo / Co-orientador: Jorge Manuel M. C. Pereira Batista / Banca: Carlos Roberto Minussi / Banca: Ricardo Luiz Barros de Freitas / Banca: Díbio Leandro Borges / Banca: Gelson da Cruz Junior / Resumo: O presente trabalho tem como objetivo realizar o reconhecimento de identidade por meio de um método baseado nos Sistemas Imunológicos Artificiais de Seleção Negativa. Para isso, foram explorados os tipos de recursos e alternativas adequadas para a análise de expressões faciais 3D, abordando a técnica de Padrão Binário que tem sido aplicada com sucesso para o problema 2D. Inicialmente, a geometria facial 3D foi convertida em duas representações em 2D, a Depth Map e a APDI, que foram implementadas com uma variedade de tipos de recursos, tais como o Local Phase Quantisers, Gabor Filters e Monogenic Filters, a fim de produzir alguns descritores para então fazer-se a análise de expressões faciais. Posteriormente, aplica-se o Algoritmo de Seleção Negativa onde são realizadas comparações e análises entre as imagens e os detectores previamente criados. Havendo afinidade entre as imagens previamente estabelecidas pelo operador, a imagem é classificada. Esta classificação é chamada de casamento. Por fim, para validar e avaliar o desempenho do método foram realizados testes com imagens diretamente da base de dados e posteriormente com dez descritores desenvolvidos a partir dos padrões binários. Esses tipos de testes foram realizados tendo em vista três objetivos: avaliar quais os melhores descritores e as melhores expressões para se realizar o reconhecimento de identidade e, por fim, validar o desempenho da nova solução de reconhecimento de identidades baseado nos Sistemas Imunológicos Artificiais. Os resultados obtidos pelo método apresentaram eficiência, robustez e precisão no reconhecimento de identidade facial / Abstract: This work aims to perform the identity recognition by a method based on Artificial Immune Systems, the Negative Selection Algorithm. Thus, the resources and adequate alternatives for analyzing 3D facial expressions were explored, exploring the Binary Pattern technique that is successfully applied for the 2D problem. Firstly, the 3D facial geometry was converted in two 2D representations. The Depth Map and the Azimuthal Projection Distance Image were implemented with other resources such as the Local Phase Quantisers, Gabor Filters and Monogenic Filters to produce descriptors to perform the facial expression analysis. Afterwards, the Negative Selection Algorithm is applied, and comparisons and analysis with the images and the detectors previously created are done. If there is affinity with the images, than the image is classified. This classification is called matching. Finally, to validate and evaluate the performance of the method, tests were realized with images from the database and after with ten descriptors developed from the binary patterns. These tests aim to: evaluate which are the best descriptors and the best expressions to recognize the identities, and to validate the performance of the new solution of identity recognition based on Artificial Immune Systems. The results show efficiency, robustness and precision in recognizing facial identity / Doutor

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Face recognition is a complex and difficult process due to various factors such as variability of illumination, occlusion, face specific characteristics like hair, glasses, beard, etc., and other similar problems affecting computer vision problems. Using a system that offers robust and consistent results for face recognition, various applications such as identification for law enforcement, secure system access, computer human interaction, etc., can be automated successfully. Different methods exist to solve the face recognition problem. Principal component analysis, Independent component analysis, and linear discriminant analysis are few other statistical techniques that are commonly used in solving the face recognition problem. Genetic algorithm, elastic bunch graph matching, artificial neural network, etc. are few of the techniques that have been proposed and implemented. The objective of this thesis paper is to provide insight into different methods available for face recognition, and explore methods that provided an efficient and feasible solution. Factors affecting the result of face recognition and the preprocessing steps that eliminate such abnormalities are also discussed briefly. Principal Component Analysis (PCA) is the most efficient and reliable method known for at least past eight years. Elastic bunch graph matching (EBGM) technique is one of the promising techniques that we studied in this thesis work. We also found better results with EBGM method than PCA in the current thesis paper. We recommend use of a hybrid technique involving the EBGM algorithm to obtain better results. Though, the EBGM method took a long time to train and generate distance measures for the given gallery images compared to PCA. But, we obtained better cumulative match score (CMS) results for the EBGM in comparison to the PCA method. Other promising techniques that can be explored separately in other paper include Genetic algorithm based methods, Mixture of principal components, and Gabor wavelet techniques.

The technology for driver safety has been developed in many fields such as airbag system, Anti-lock Braking System or ABS, ultrasonic warning system, and others. Recently, some of the automobile companies have introduced a new feature of driver safety systems. This new system is to make the car slower if it finds a driver’s drowsy eyes. For instance, Toyota Motor Corporation announced that it has given its pre-crash safety system the ability to determine whether a driver’s eyes are properly open with an eye monitor. This paper is focusing on finding a driver’s drowsy eyes by using face detection technology. The human face is a dynamic object and has a high degree of variability; that is why face detection is considered a difficult problem in computer vision. Even with the difficulty of this problem, scientists and computer programmers have developed and improved the face detection technologies. This paper also introduces some algorithms to find faces or eyes and compares algorithm’s characteristics. Once we find a face in a sequence of images, the matter is to find drowsy eyes in the driver safety system. This system can slow a car or alert the user not to sleep; that is the purpose of the pre-crash safety system. This paper introduces the VeriLook SDK, which is used for finding a driver’s face in the real time driver safety system. With several experiments, this paper also introduces a new way to find drowsy eyes by AOI,Area of Interest. This algorithm improves the speed of finding drowsy eyes and the consumption of memory use without using any object classification methods or matching eye templates. Moreover, this system has a higher accuracy of classification than others.

Thesis (Ph. D.)--University of Florida, 2001. / Title from first page of PDF file. Document formatted into pages; contains xi, 124 p.; also contains graphics. Vita. Includes bibliographical references (p. 120-123).

Automatic face recognition has attracted the attention of many research institutes, commercial industries, and government agencies in the past few years mainly due to the emergence of numerous applications, such as surveillance, access control to secure facilities, and airport screening. Almost all of the research on the early days of face recognition was focused on using 2-D (intensity/portrait) images of the face. While several sophisticated 2-D solutions have been proposed, unbiased evaluation studies show that their collective performance remains unsatisfactory, and degrades significantly with variations in lighting condition, face position, makeup, or existence of non-neutral facial expressions. Recent developments in 3-D imaging technology has made cheaper, quicker and more reliable acquisition of 3-D facial models a reality. These 3-D facial models contain information about the anatomical structure of the face that remains constant under variable lighting conditions, facial makeup, and pose variations. Thus, researchers are considering to utilize 3-D structure of the face alone or in combination with 2-D information to alleviate inherent limitations of 2-D images and attain better performance. Published 3-D face recognition algorithms have demonstrated promising results confirming the effectiveness of 3-D facial models in dealing with the above mentioned factors contributing to the failure of 2-D face recognition systems. However, the majority of these 3-D algorithms are extensions of conventional 2-D approaches, where intensity images are simply replaced by 3-D models rendered as range images. These algorithms are not specifically tailored to exploit abundant geometric and anthropometric clues available in 3-D facial models. In this dissertation we introduce innovative 3-D and 2-D+3-D facial measurements (features) that effectively describe the geometric characteristics of the corresponding faces. Some of the features described in this dissertation, as well as many features proposed in the literature are defined around or between meaningful facial landmarks (fiducial points). In order to reach our goal of designing an accurate automatic face recognition system, we also propose a novel algorithm combining 3-D (range) and 2-D (portrait) Gabor clues to pinpoint a number of points with meaningful anthropometric definitions with significantly better accuracies than those achievable using a single modality alone. This dissertation is organized as follows. In Chapter 1, various biometric modalities are introduced and the advantages of the facial biometrics over other modalities are discussed. The discussion in Chapter 1 is continued with introduction of the face recognition’s modes of operation followed by some current and potential future applications. The problem statement of this dissertation is also included in this chapter. In Chapter 2, an extensive review of the successful 2-D, 3-D, and 2-D+3-D face recognition algorithms are provided. Chapter 3 presents the details of our innovative 3-D and 2-D+3-D face features, as well as our accurate fiducial point detection algorithm. Conclusions and directions for future extensions are presented in Chapter 4. / text