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This thesis establishes a framework for facial feature detection and human face movement tracking. Statistical models of shape and appearance are built to represent the human face structure and interpret target images of human faces. The approach is a patch-based method derived from an earlier proposed method, the constrained local model (CLM) [1] algorithm. In order to increase the ability to track face movements with large head rotations, a 3D shape model is used in the system. And multiple texture models from different viewpoints are used to model the appearance. During fitting or tracking, the current estimate of pose (shape coordinates) is used to select the appropriate texture model. The algorithm uses the shape model and a texture model to generate a set of region template detectors. A search is then performed in the global pose / shape space using these detectors. Different optimisation frameworks are used in the implementation. The training images are created by rendering expressive 3D face models with different scales, rotations, expressions, brightness, etc. Experimental results are demonstrated by fitting the model to image sequences with large head rotations to evaluate the performance of the algorithm. To evaluate the stability and selection of factors of the algorithm, more experiments are carried out. The results show that the proposed 3D constrained local model algorithm improves the performance of the original CLM algorithm for videos with large out-of-plane head rotations.

This study aimed to understand the differences in strength or coordination of brain regions involved in processing faces in the presence of aging and/or progressing neuropathology (Alzheimer's disease). To this end, Experiment 1 evaluated age-related differences in basic face processing and the effects of familiarity in face processing. Overall, face processing in younger (22-35yrs) and older participants (63-83yrs) recruited a broadly distributed network of brain activity, but the distribution of activity varied depending on the age of the individual. The younger population utilized regions of the occipitotemporal, medial frontal and posterior parietal cortices while the older population recruited a concentrated occipitotemporal network. The younger participants were also sensitive to the type of face presented, as Novel faces were associated with greater mean BOLD activity than either the Famous or Relatives faces. Interestingly, Relatives faces were associated with greater mean B OLD activity in more regions of the brain than found in any other analysis in Exp. 1, spanning the inferior frontal, medial temporal and inferior parietal cortices. In contrast, the older adults were not sensitive to the type of face presented, which could reflect a difference in cognitive strategies used by the older population when presented with this type of face stimuli. Experiment 2 evaluated face processing, familiarity in face processing and also emphasized the interactive roles autobiographical processing and memory recency play in processing familiar faces in mature adults (MA; 45-55yrs), older adults (OA; 70-92yrs) and patients suffering from Alzheimer's disease (AD; 70-92yrs). / MA participants had greater mean BOLD activity values in more regions of the brain than observed in either of the older adult populations, spanning regions of the medial frontal, medial temporal, inferior parietal and occipital cortices. OA, in contrast, utilized a concentrated frontal and medial temporal network and AD participants had the greatest deficit in BOLD activity overall.Age-related differences in processing faces, in processing the type of face presented, in autobiographical information processing and in processing the recency of a memory were noted, as well as differences due to the deleterious effects of AD. / by Jeanna Winchester. / Thesis (Ph.D.)--Florida Atlantic University, 2009. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2009. Mode of access: World Wide Web.

Two experiments were conducted to investigate the impact of poser and perceiver gender on the Happiness/Anger Superiority effect and the Female Advantage in facial expression recognition. Happy, neutral, and angry facial expressions were presented on male and female faces under Continuous Flash Suppression (CFS). Participants of both genders indicated when the presented faces broke through the suppression. In the second experiment, angry and happy expressions were reduced to 50% intensity. At full intensity, there was no difference in the reaction time for female neutral and angry faces, but male faces showed a difference in detection between all expressions. Across experiments, male faces were detected later than female faces for all facial expressions. Happiness was generally detected faster than anger, except when on female faces at 50% intensity. No main effect for perceiver gender emerged. It was concluded that happiness is superior to anger in CFS, and that poser gender affects facial expression recognition. / by Sophia Peaco. / Thesis (M.A.)--Florida Atlantic University, 2013. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.

Many errors in recognition are made because various features of a stimulus are attended inefficiently. Those features are not bound together and can then be confused with other information. One of the most common types of these errors is conjunction errors. These happen when mismatched features of memories are combined to form a composite memory. This study tests how likely conjunction errors, along with other recognition errors, occur when participants watch videos of people both with and without unusual facial features performing actions after a week time lag. It was hypothesized that participants would falsely recognize actresses in the conjunction item condition over the other conditions. The likelihood of falsely recognizing a new person increased when presented with a feature, but the conjunction items overall were most often falsely recognized. / by Autumn Keif. / Thesis (M.A.)--Florida Atlantic University, 2012. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2012. Mode of access: World Wide Web.

by Lau Kwok Kin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 82-87). / Abstracts in English and Chinese. / List of Figures --- p.vi / List of Tables --- p.vii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Objective of This Thesis --- p.3 / Chapter 1.2 --- Organization of This Thesis --- p.3 / Chapter 2 --- Literature Review --- p.4 / Chapter 2.1 --- Facial Memory Recall --- p.4 / Chapter 2.2 --- Facial Recognition --- p.6 / Chapter 2.2.1 --- Earlier Approaches --- p.7 / Chapter 2.2.2 --- Feature and Template Matching --- p.8 / Chapter 2.2.3 --- Neural Network --- p.10 / Chapter 2.2.4 --- Statistical Approach --- p.14 / Chapter 3 --- A Forensic Application of Facial Recall --- p.19 / Chapter 3.1 --- Motivation --- p.20 / Chapter 3.2 --- AICAMS-FIT --- p.20 / Chapter 3.2.1 --- The Facial Component Library --- p.21 / Chapter 3.2.2 --- The Feature Selection Module --- p.24 / Chapter 3.2.3 --- The Facial Construction Module --- p.24 / Chapter 3.3 --- The Interaction Between The Three Main Components --- p.29 / Chapter 3.4 --- Summary --- p.30 / Chapter 4 --- Sketch-to-Sketch Matching --- p.31 / Chapter 4.1 --- The Representation of A Composite Face --- p.31 / Chapter 4.2 --- The Component-based Encoding Scheme --- p.32 / Chapter 4.2.1 --- Local Feature Analysis --- p.34 / Chapter 4.2.2 --- Similarity Matrix --- p.36 / Chapter 4.3 --- Experimental Results and Evaluation --- p.41 / Chapter 4.4 --- Shortcomings of the encoding scheme --- p.44 / Chapter 4.4.1 --- Size Variation --- p.45 / Chapter 4.5 --- Summary --- p.51 / Chapter 5 --- Sketch-to-Photo/Photo-to-Sketch Matching --- p.52 / Chapter 5.1 --- Principal Component Analysis --- p.53 / Chapter 5.2 --- Experimental Setup --- p.56 / Chapter 5.3 --- Experimental Results --- p.59 / Chapter 5.3.1 --- Sketch-to-Photo Matching --- p.59 / Chapter 5.3.2 --- Photo-to-Sketch Matching --- p.62 / Chapter 5.4 --- Summary --- p.66 / Chapter 6 --- Future Work --- p.67 / Chapter 7 --- Conclusions --- p.70 / Chapter A --- Image Library I --- p.72 / Chapter A.1 --- The Database for Searching --- p.72 / Chapter A.2 --- The Database for Testing --- p.74 / Chapter B --- Image Library II --- p.75 / Chapter B.1 --- The Photographic Database --- p.75 / Chapter B.2 --- The Sketch Database --- p.77 / Chapter C --- The Eigenfaces --- p.78 / Chapter C.1 --- Eigenfaces of Photographic Database (N = 20) --- p.78 / Chapter C.2 --- Eigenfaces of Photographic Database (N = 100) --- p.79 / Chapter C.3 --- The Eigenfaces of Sketch Database --- p.81 / Bibliography --- p.82

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.