Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition

This paper presents a novel quadratic projection based feature extraction framework, where a set of quadratic matrices is learned to distinguish each class from all other classes. We formulate quadratic matrix learning (QML) as a standard semidefinite programming (SDP) problem. However, the con- ventional interior-point SDP solvers do not scale well to the problem of QML for high-dimensional data. To solve the scalability of QML, we develop an efficient algorithm, termed DualQML, based on the Lagrange duality theory, to extract nonlinear features. To evaluate the feasibility and effectiveness of the proposed framework, we conduct extensive experiments on biometric recognition. Experimental results on three representative biometric recogni- tion tasks, including face, palmprint, and ear recognition, demonstrate the superiority of the DualQML-based feature extraction algorithm compared to the current state-of-the-art algorithm

Personalized Automatic Estimation of Self-reported Pain Intensity from Facial Expressions

Pain is a personal, subjective experience that is commonly evaluated through visual analog scales (VAS). While this is often convenient and useful, automatic pain detection systems can reduce pain score acquisition efforts in large-scale studies by estimating it directly from the participants' facial expressions. In this paper, we propose a novel two-stage learning approach for VAS estimation: first, our algorithm employs Recurrent Neural Networks (RNNs) to automatically estimate Prkachin and Solomon Pain Intensity (PSPI) levels from face images. The estimated scores are then fed into the personalized Hidden Conditional Random Fields (HCRFs), used to estimate the VAS, provided by each person. Personalization of the model is performed using a newly introduced facial expressiveness score, unique for each person. To the best of our knowledge, this is the first approach to automatically estimate VAS from face images. We show the benefits of the proposed personalized over traditional non-personalized approach on a benchmark dataset for pain analysis from face images.Comment: Computer Vision and Pattern Recognition Conference, The 1st International Workshop on Deep Affective Learning and Context Modelin

Discriminative Appearance Models for Face Alignment

The proposed face alignment algorithm uses local gradient features as the appearance representation. These features are obtained by pixel value comparison, which provide robustness against changes in illumination, as well as partial occlusion and local deformation due to the locality. The adopted features are modeled in three discriminative methods, which correspond to different alignment cost functions. The discriminative appearance modeling alleviate the generalization problem to some extent

Essays on distance metric learning

Many machine learning methods, such as the k-nearest neighbours algorithm, heavily depend on the distance measure between data points. As each task has its own notion of distance, distance metric learning has been proposed. It learns a distance metric to assign a small distance to semantically similar instances and a large distance to dissimilar instances by formulating an optimisation problem. While many loss functions and regularisation terms have been proposed to improve the discrimination and generalisation ability of the learned metric, the metric may be sensitive to a small perturbation in the input space. Moreover, these methods implicitly assume that features are numerical variables and labels are deterministic. However, categorical variables and probabilistic labels are common in real-world applications. This thesis develops three metric learning methods to enhance robustness against input perturbation and applicability for categorical variables and probabilistic labels. In Chapter 3, I identify that many existing methods maximise a margin in the feature space and such margin is insufficient to withstand perturbation in the input space. To address this issue, a new loss function is designed to penalise the input-space margin for being small and hence improve the robustness of the learned metric. In Chapter 4, I propose a metric learning method for categorical data. Classifying categorical data is difficult due to high feature ambiguity, and to this end, the technique of adversarial training is employed. Moreover, the generalisation bound of the proposed method is established, which informs the choice of the regularisation term. In Chapter 5, I adapt a classical probabilistic approach for metric learning to utilise information on probabilistic labels. The loss function is modified for training stability, and new evaluation criteria are suggested to assess the effectiveness of different methods. At the end of this thesis, two publications on hyperspectral target detection are appended as additional work during my PhD

Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation

Direct prediction of 3D body pose and shape remains a challenge even for highly parameterized deep learning models. Mapping from the 2D image space to the prediction space is difficult: perspective ambiguities make the loss function noisy and training data is scarce. In this paper, we propose a novel approach (Neural Body Fitting (NBF)). It integrates a statistical body model within a CNN, leveraging reliable bottom-up semantic body part segmentation and robust top-down body model constraints. NBF is fully differentiable and can be trained using 2D and 3D annotations. In detailed experiments, we analyze how the components of our model affect performance, especially the use of part segmentations as an explicit intermediate representation, and present a robust, efficiently trainable framework for 3D human pose estimation from 2D images with competitive results on standard benchmarks. Code will be made available at http://github.com/mohomran/neural_body_fittingComment: 3DV 201

The socioeconomic pattern of health and developmental outcomes among Aboriginal and Torres Strait Islander children

The pervasive health and social disadvantage faced by Aboriginal and Torres Strait Islander peoples is an acknowledged part of Australian society. The contemporary data reveal striking inequalities between Indigenous and non-Indigenous Australians in most measurable aspects of wellbeing across the life cycle. This reflects a postcolonial history of marginalisation and exclusion from mainstream society, dispossession of traditional lands, forced separation from family and kinship networks, and racism. Despite an increased awareness and disapproval of these inequalities in health, the inequalities persist.The lack of progress in the face of public disapproval and progressive government support underscores the fact that we still do not adequately understand the fundamental causes of Indigenous ill health and disease. A small body of research in Australia has highlighted that socioeconomic status (SES) accounts for a portion of the gap in health but this does not imply that they account for health differences within Indigenous population groups. A robust international literature has consistently shown that socioeconomic factors influence population health. These factors reflect the way in which society is ordered according to wealth, prestige, power, social standing or one’s control over economic resources, and their pattern of association with health has almost always depicted better health for those who are better off— that is, the health of population groups normally follows a gradient pattern. Despite the ubiquity of this observation in the empirical literature, there is uncertainty as to whether it applies to Aboriginal and Torres Strait Islander populations in Australia.Accordingly, this thesis has aimed to assess the pattern of socioeconomic disparities in the health and development of Indigenous populations in Australia, with a specific focus on children. The three key objectives were to: • Describe the developmental status of Indigenous children and the mechanisms that influence this status; • Determine the pattern of association between socioeconomic factors and physical and mental health outcomes; and • Reveal the significant differences (and similarities) in the socioeconomic pattern of child health between Indigenous and non-Indigenous populations, and articulate these in terms of their direction, shape and magnitude.The objectives of the study were primarily assessed using a quantitative analytic framework applied to four existing population-representative datasets: the 2008 National Aboriginal and Torres Strait Islander Social Survey, the 2000–2002 Western Australian Aboriginal Child Health Survey, the 2004–05 National Aboriginal and Torres Strait Islander Health Survey and 2004–05 National Health Survey. Simple univariate and cross-tabulation data were used to describe population characteristics, while the relationships between socioeconomic indicators and health outcomes were assessed using a range of regression techniques. Multilevel models are an important feature of this study, and have enabled a more accurate estimation of the effects of individual and area-level measures of SES on health. Generalised Additive Models were used to account for the possible non-linear nature of associations between continuous SES variables and physical health outcomes, with results presented as non-parametric spline curves. The mechanisms linking SES and mental health were explored using a stepwise approach to the regression analysis. All data in all chapters were weighted to reflect population benchmarks.The findings highlighted that there were significant socioeconomic disparities in the health of Indigenous children in Australia, although the direction, shape and magnitude varied, by both socioeconomic measure and health outcome. While the socioeconomic patterns of Indigenous child health are not universal, they are more consistent for mental than physical health. In addition, the thesis has shown that both conventional and alternative notions of SES can influence health patterns. The largest disparities in child physical health were observed for area-level SES indicators, while housing characteristics and area-level SES both had a strong direct effect on child mental health.The thesis has demonstrated that the patterns of socioeconomic disparities in child health differ markedly in Indigenous and non-Indigenous populations—at least in non-remote settings. It was not uncommon for the magnitude of disparity to be larger in the Indigenous population. These findings lend support to the notion that socioeconomic factors have a differential impact on the health of Indigenous and non-Indigenous populations. The implication of this for policy is that a single approach to stimulating socioeconomic conditions will not have equal benefits to child health outcomes in Indigenous and non-Indigenous populations. While the evidence here underscores the validity of the well-worn edict that “one size does not fit all” in Indigenous health policy, it also reinforces the need to examine health disparities within and across Indigenous and other population groups in order to better inform policy and practiceCollectively, the results have provided clear evidence that socioeconomic factors matter to both the physical and mental health of Aboriginal and Torres Strait Islander children. The diversity of findings implies that SES factors are one facet of the unique and complex set of factors that influence Aboriginal child health and wellbeing.This thesis has made several original contributions to the literature on social inequalities in Indigenous health in Australia and the broader field of social determinants of health. It is one of the few studies internationally to explicitly look at the socioeconomic patterning of health in an Indigenous population, and the first to examine these patterns among Indigenous children using population-representative data. In doing so, the study has begun to bridge the knowledge gap on social inequalities in Aboriginal health in Australia, and will facilitate a better grasp of the complex underlying mechanisms that determine Aboriginal health.For policy, this knowledge can lead to more effective government decision-making in terms of targeting social determinants of health that are of particular significance for Aboriginal populations. It is hoped that the findings of the thesis can provide directions for future research and insights to policy that will, ultimately, increase the pace of change toward health equity in Australia