62 research outputs found
Structured representation learning from complex data
This thesis advances several theoretical and practical aspects of the recently introduced restricted Boltzmann machine - a powerful probabilistic and generative framework for modelling data and learning representations. The contributions of this study represent a systematic and common theme in learning structured representations from complex data
Statistical modelling for facial expression dynamics
PhDOne of the most powerful and fastest means of relaying emotions between humans are facial expressions.
The ability to capture, understand and mimic those emotions and their underlying dynamics
in the synthetic counterpart is a challenging task because of the complexity of human emotions, different
ways of conveying them, non-linearities caused by facial feature and head motion, and the
ever critical eye of the viewer. This thesis sets out to address some of the limitations of existing
techniques by investigating three components of expression modelling and parameterisation framework:
(1) Feature and expression manifold representation, (2) Pose estimation, and (3) Expression
dynamics modelling and their parameterisation for the purpose of driving a synthetic head avatar.
First, we introduce a hierarchical representation based on the Point Distribution Model (PDM).
Holistic representations imply that non-linearities caused by the motion of facial features, and intrafeature
correlations are implicitly embedded and hence have to be accounted for in the resulting
expression space. Also such representations require large training datasets to account for all possible
variations. To address those shortcomings, and to provide a basis for learning more subtle, localised
variations, our representation consists of tree-like structure where a holistic root component is decomposed
into leaves containing the jaw outline, each of the eye and eyebrows and the mouth. Each
of the hierarchical components is modelled according to its intrinsic functionality, rather than the
final, holistic expression label.
Secondly, we introduce a statistical approach for capturing an underlying low-dimension expression
manifold by utilising components of the previously defined hierarchical representation. As
Principal Component Analysis (PCA) based approaches cannot reliably capture variations caused by
large facial feature changes because of its linear nature, the underlying dynamics manifold for each
of the hierarchical components is modelled using a Hierarchical Latent Variable Model (HLVM) approach.
Whilst retaining PCA properties, such a model introduces a probability density model which
can deal with missing or incomplete data and allows discovery of internal within cluster structures.
All of the model parameters and underlying density model are automatically estimated during the
training stage. We investigate the usefulness of such a model to larger and unseen datasets.
Thirdly, we extend the concept of HLVM model to pose estimation to address the non-linear
shape deformations and definition of the plausible pose space caused by large head motion. Since
our head rarely stays still, and its movements are intrinsically connected with the way we perceive
and understand the expressions, pose information is an integral part of their dynamics. The proposed
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approach integrates into our existing hierarchical representation model. It is learned using sparse and
discreetly sampled training dataset, and generalises to a larger and continuous view-sphere.
Finally, we introduce a framework that models and extracts expression dynamics. In existing
frameworks, explicit definition of expression intensity and pose information, is often overlooked,
although usually implicitly embedded in the underlying representation. We investigate modelling
of the expression dynamics based on use of static information only, and focus on its sufficiency
for the task at hand. We compare a rule-based method that utilises the existing latent structure and
provides a fusion of different components with holistic and Bayesian Network (BN) approaches. An
Active Appearance Model (AAM) based tracker is used to extract relevant information from input
sequences. Such information is subsequently used to define the parametric structure of the underlying
expression dynamics. We demonstrate that such information can be utilised to animate a synthetic
head avatar.
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Application of Prior Information to Discriminative Feature Learning
Learning discriminative feature representations has attracted a great deal of attention since it is a critical step to facilitate the subsequent classification, retrieval and recommendation tasks. In this dissertation, besides incorporating prior knowledge about image labels into the image classification as most prevalent feature learning methods currently do, we also explore some other general-purpose priors and verify their effectiveness in the discriminant feature learning. As a more powerful representation can be learned by implementing such general priors, our approaches achieve state-of-the-art results on challenging benchmarks. We elaborate on these general-purpose priors and highlight where we have made novel contributions.
We apply sparsity and hierarchical priors to the explanatory factors that describe the data, in order to better discover the data structure. More specifically, in the first approach we propose that we only incorporate sparse priors into the feature learning. To this end, we present a support discrimination dictionary learning method, which finds a dictionary under which the feature representation of images from the same class have a common sparse structure while the size of the overlapped signal support of different classes is minimised. Then we incorporate sparse priors and hierarchical priors into a unified framework, that is capable of controlling the sparsity of the neuron activation in deep neural networks. Our proposed approach automatically selects the most useful low-level features and effectively combines them into more powerful and discriminative features for our specific image classification problem.
We also explore priors on the relationships between multiple factors. When multiple independent factors exist in the image generation process and only some of them are of interest to us, we propose a novel multi-task adversarial network to learn a disentangled feature which is optimized with respect to the factor of interest to us, while being distraction factors agnostic. When common factors exist in multiple tasks, leveraging common factors cannot only make the learned feature representation more robust, but also enable the model to generalise from very few labelled samples. More specifically, we address the domain adaptation problem and propose the re-weighted adversarial adaptation network to reduce the feature distribution divergence and adapt the classifier from source to target domains
Sparse representation for face images.
This thesis address issues for face recognition with multi-view face images. Several effective methods are proposed and compared with current state of the art. A novel framework that generalises existing sparse representation-based methods in order to exploit the sharing information to against pose variations of face images is proposed
Machine Learning
Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience
Re-identifying people in the crowd
Developing an automated surveillance system is of great interest for various reasons including forensic and security applications. In the case of a network of surveillance cameras with non-overlapping fields of view, person detection and tracking alone are insufficient to track a subject of interest across the network. In this case, instances of a person captured in one camera view need to be retrieved among a gallery of different people, in other camera views. This vision problem is commonly known as person re-identification (re-id).
Cross-view instances of pedestrians exhibit varied levels of illumination, viewpoint, and pose variations which makes the problem very challenging. Despite recent progress towards improving accuracy, existing systems suffer from low applicability to real-world scenarios. This is mainly caused by the need for large amounts of annotated data from pairwise camera views to be available for training. Given the difficulty of obtaining such data and annotating it, this thesis aims to bring the person re-id problem a step closer to real-world deployment.
In the first contribution, the single-shot protocol, where each individual is represented by a pair of images that need to be matched, is considered. Following the extensive annotation of four datasets for six attributes, an evaluation of the most widely used feature extraction schemes is conducted. The results reveal two high-performing descriptors among those evaluated, and show illumination variation to have the most impact on re-id accuracy.
Motivated by the wide availability of videos from surveillance cameras and the additional visual and temporal information they provide, video-based person re-id is then investigated, and a su-pervised system is developed. This is achieved by improving and extending the best performing image-based person descriptor into three dimensions and combining it with distance metric learn-ing. The system obtained achieves state-of-the-art results on two widely used datasets.
Given the cost and difficulty of obtaining labelled data from pairwise cameras in a network to train the model, an unsupervised video-based person re-id method is also developed. It is based on a set-based distance measure that leverages rank vectors to estimate the similarity scores between person tracklets. The proposed system outperforms other unsupervised methods by a large margin on two datasets while competing with deep learning methods on another large-scale dataset
Facial expression recognition in the wild : from individual to group
The progress in computing technology has increased the demand for smart systems capable of understanding human affect and emotional manifestations. One of the crucial factors in designing systems equipped with such intelligence is to have accurate automatic Facial Expression Recognition (FER) methods. In computer vision, automatic facial expression analysis is an active field of research for over two decades now. However, there are still a lot of questions unanswered. The research presented in this thesis attempts to address some of the key issues of FER in challenging conditions mentioned as follows: 1) creating a facial expressions database representing real-world conditions; 2) devising Head Pose Normalisation (HPN) methods which are independent of facial parts location; 3) creating automatic methods for the analysis of mood of group of people. The central hypothesis of the thesis is that extracting close to real-world data from movies and performing facial expression analysis on movies is a stepping stone in the direction of moving the analysis of faces towards real-world, unconstrained condition. A temporal facial expressions database, Acted Facial Expressions in the Wild (AFEW) is proposed. The database is constructed and labelled using a semi-automatic process based on closed caption subtitle based keyword search. Currently, AFEW is the largest facial expressions database representing challenging conditions available to the research community. For providing a common platform to researchers in order to evaluate and extend their state-of-the-art FER methods, the first Emotion Recognition in the Wild (EmotiW) challenge based on AFEW is proposed. An image-only based facial expressions database Static Facial Expressions In The Wild (SFEW) extracted from AFEW is proposed. Furthermore, the thesis focuses on HPN for real-world images. Earlier methods were based on fiducial points. However, as fiducial points detection is an open problem for real-world images, HPN can be error-prone. A HPN method based on response maps generated from part-detectors is proposed. The proposed shape-constrained method does not require fiducial points and head pose information, which makes it suitable for real-world images. Data from movies and the internet, representing real-world conditions poses another major challenge of the presence of multiple subjects to the research community. This defines another focus of this thesis where a novel approach for modeling the perception of mood of a group of people in an image is presented. A new database is constructed from Flickr based on keywords related to social events. Three models are proposed: averaging based Group Expression Model (GEM), Weighted Group Expression Model (GEM_w) and Augmented Group Expression Model (GEM_LDA). GEM_w is based on social contextual attributes, which are used as weights on each person's contribution towards the overall group's mood. Further, GEM_LDA is based on topic model and feature augmentation. The proposed framework is applied to applications of group candid shot selection and event summarisation. The application of Structural SIMilarity (SSIM) index metric is explored for finding similar facial expressions. The proposed framework is applied to the problem of creating image albums based on facial expressions, finding corresponding expressions for training facial performance transfer algorithms
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