Learning how to be robust: Deep polynomial regression

Polynomial regression is a recurrent problem with a large number of applications. In computer vision it often appears in motion analysis. Whatever the application, standard methods for regression of polynomial models tend to deliver biased results when the input data is heavily contaminated by outliers. Moreover, the problem is even harder when outliers have strong structure. Departing from problem-tailored heuristics for robust estimation of parametric models, we explore deep convolutional neural networks. Our work aims to find a generic approach for training deep regression models without the explicit need of supervised annotation. We bypass the need for a tailored loss function on the regression parameters by attaching to our model a differentiable hard-wired decoder corresponding to the polynomial operation at hand. We demonstrate the value of our findings by comparing with standard robust regression methods. Furthermore, we demonstrate how to use such models for a real computer vision problem, i.e., video stabilization. The qualitative and quantitative experiments show that neural networks are able to learn robustness for general polynomial regression, with results that well overpass scores of traditional robust estimation methods.Comment: 18 pages, conferenc

DeepCoder: Semi-parametric Variational Autoencoders for Automatic Facial Action Coding

Human face exhibits an inherent hierarchy in its representations (i.e., holistic facial expressions can be encoded via a set of facial action units (AUs) and their intensity). Variational (deep) auto-encoders (VAE) have shown great results in unsupervised extraction of hierarchical latent representations from large amounts of image data, while being robust to noise and other undesired artifacts. Potentially, this makes VAEs a suitable approach for learning facial features for AU intensity estimation. Yet, most existing VAE-based methods apply classifiers learned separately from the encoded features. By contrast, the non-parametric (probabilistic) approaches, such as Gaussian Processes (GPs), typically outperform their parametric counterparts, but cannot deal easily with large amounts of data. To this end, we propose a novel VAE semi-parametric modeling framework, named DeepCoder, which combines the modeling power of parametric (convolutional) and nonparametric (ordinal GPs) VAEs, for joint learning of (1) latent representations at multiple levels in a task hierarchy1, and (2) classification of multiple ordinal outputs. We show on benchmark datasets for AU intensity estimation that the proposed DeepCoder outperforms the state-of-the-art approaches, and related VAEs and deep learning models.Comment: ICCV 2017 - accepte

Novel Deep Learning Techniques For Computer Vision and Structure Health Monitoring

This thesis proposes novel techniques in building a generic framework for both the regression and classification tasks in vastly different applications domains such as computer vision and civil engineering. Many frameworks have been proposed and combined into a complex deep network design to provide a complete solution to a wide variety of problems. The experiment results demonstrate significant improvements of all the proposed techniques towards accuracy and efficiency

A survey of face recognition techniques under occlusion

The limited capacity to recognize faces under occlusions is a long-standing problem that presents a unique challenge for face recognition systems and even for humans. The problem regarding occlusion is less covered by research when compared to other challenges such as pose variation, different expressions, etc. Nevertheless, occluded face recognition is imperative to exploit the full potential of face recognition for real-world applications. In this paper, we restrict the scope to occluded face recognition. First, we explore what the occlusion problem is and what inherent difficulties can arise. As a part of this review, we introduce face detection under occlusion, a preliminary step in face recognition. Second, we present how existing face recognition methods cope with the occlusion problem and classify them into three categories, which are 1) occlusion robust feature extraction approaches, 2) occlusion aware face recognition approaches, and 3) occlusion recovery based face recognition approaches. Furthermore, we analyze the motivations, innovations, pros and cons, and the performance of representative approaches for comparison. Finally, future challenges and method trends of occluded face recognition are thoroughly discussed

A survey of face recognition techniques under occlusion

The limited capacity to recognize faces under occlusions is a long-standing problem that presents a unique challenge for face recognition systems and even for humans. The problem regarding occlusion is less covered by research when compared to other challenges such as pose variation, different expressions, etc. Nevertheless, occluded face recognition is imperative to exploit the full potential of face recognition for real-world applications. In this paper, we restrict the scope to occluded face recognition. First, we explore what the occlusion problem is and what inherent difficulties can arise. As a part of this review, we introduce face detection under occlusion, a preliminary step in face recognition. Second, we present how existing face recognition methods cope with the occlusion problem and classify them into three categories, which are 1) occlusion robust feature extraction approaches, 2) occlusion aware face recognition approaches, and 3) occlusion recovery based face recognition approaches. Furthermore, we analyze the motivations, innovations, pros and cons, and the performance of representative approaches for comparison. Finally, future challenges and method trends of occluded face recognition are thoroughly discussed

Learning how to be robust: Deep polynomial regression

Polynomial regression is a recurrent problem with a large number of applications. In computer vision it often appears in motion analysis. Whatever the application, standard methods for regression of polynomial models tend to deliver biased results when the input data is heavily contaminated by outliers. Moreover, the problem is even harder when outliers have strong structure. Departing from problem-tailored heuristics for robust estimation of parametric models, we explore deep convolutional neural networks. Our work aims to find a generic approach for training deep regression models without the explicit need of supervised annotation. We bypass the need for a tailored loss function on the regression parameters by attaching to our model a differentiable hard-wired decoder corresponding to the polynomial operation at hand. We demonstrate the value of our findings by comparing with standard robust regression methods. Furthermore, we demonstrate how to use such models for a real computer vision problem, i.e., video stabilization. The qualitative and quantitative experiments show that neural networks are able to learn robustness for general polynomial regression, with results that well overpass scores of traditional robust estimation methods

Machine Learning in Robotic Navigation:Deep Visual Localization and Adaptive Control

The work conducted in this thesis contributes to the robotic navigation field by focusing on different machine learning solutions: supervised learning with (deep) neural networks, unsupervised learning, and reinforcement learning.First, we propose a semi-supervised machine learning approach that can dynamically update the robot controller's parameters using situational analysis through feature extraction and unsupervised clustering. The results show that the robot can adapt to the changes in its surroundings, resulting in a thirty percent improvement in navigation speed and stability.Then, we train multiple deep neural networks for estimating the robot's position in the environment using ground truth information provided by a classical localization and mapping approach. We prepare two image-based localization datasets in 3D simulation and compare the results of a traditional multilayer perceptron, a stacked denoising autoencoder, and a convolutional neural network (CNN). The experiment results show that our proposed inception based CNNs without pooling layers perform very well in all the environments. Finally, we propose a two-stage learning framework for visual navigation in which the experience of the agent during exploration of one goal is shared to learn to navigate to other goals. The multi-goal Q-function learns to traverse the environment by using the provided discretized map. Transfer learning is applied to the multi-goal Q-function from a maze structure to a 2D simulator and is finally deployed in a 3D simulator where the robot uses the estimated locations from the position estimator deep CNNs. The results show a significant improvement when multi-goal reinforcement learning is used

Feature extraction on faces : from landmark localization to depth estimation

Le sujet de cette thèse porte sur les algorithmes d'apprentissage qui extraient les caractéristiques importantes des visages. Les caractéristiques d’intérêt principal sont des points clés; La localisation en deux dimensions (2D) ou en trois dimensions (3D) de traits importants du visage telles que le centre des yeux, le bout du nez et les coins de la bouche. Les points clés sont utilisés pour résoudre des tâches complexes qui ne peuvent pas être résolues directement ou qui requièrent du guidage pour l’obtention de performances améliorées, telles que la reconnaissance de poses ou de gestes, le suivi ou la vérification du visage. L'application des modèles présentés dans cette thèse concerne les images du visage; cependant, les algorithmes proposés sont plus généraux et peuvent être appliqués aux points clés de d'autres objets, tels que les mains, le corps ou des objets fabriqués par l'homme. Cette thèse est écrite par article et explore différentes techniques pour résoudre plusieurs aspects de la localisation de points clés. Dans le premier article, nous démêlons l'identité et l'expression d'un visage donné pour apprendre une distribution à priori sur l'ensemble des points clés. Cette distribution à priori est ensuite combinée avec un classifieur discriminant qui apprend une distribution de probabilité indépendante par point clé. Le modèle combiné est capable d'expliquer les différences dans les expressions pour une même représentation d'identité. Dans le deuxième article, nous proposons une architecture qui vise à conserver les caractéristiques d’images pour effectuer des tâches qui nécessitent une haute précision au niveau des pixels, telles que la localisation de points clés ou la segmentation d’images. L’architecture proposée extrait progressivement les caractéristiques les plus grossières dans les étapes d'encodage pour obtenir des informations plus globales sur l’image. Ensuite, il étend les caractéristiques grossières pour revenir à la résolution de l'image originale en recombinant les caractéristiques du chemin d'encodage. Le modèle, appelé Réseaux de Recombinaison, a obtenu l’état de l’art sur plusieurs jeux de données, tout en accélérant le temps d’apprentissage. Dans le troisième article, nous visons à améliorer la localisation des points clés lorsque peu d'images comportent des étiquettes sur des points clés. En particulier, nous exploitons une forme plus faible d’étiquettes qui sont plus faciles à acquérir ou plus abondantes tel que l'émotion ou la pose de la tête. Pour ce faire, nous proposons une architecture permettant la rétropropagation du gradient des étiquettes les plus faibles à travers des points clés, ainsi entraînant le réseau de localisation des points clés. Nous proposons également une composante de coût non supervisée qui permet des prédictions de points clés équivariantes en fonction des transformations appliquées à l'image, sans avoir les vraies étiquettes des points clés. Ces techniques ont considérablement amélioré les performances tout en réduisant le pourcentage d'images étiquetées par points clés. Finalement, dans le dernier article, nous proposons un algorithme d'apprentissage permettant d'estimer la profondeur des points clés sans aucune supervision de la profondeur. Nous y parvenons en faisant correspondre les points clés de deux visages en les transformant l'un vers l'autre. Cette transformation nécessite une estimation de la profondeur sur un visage, ainsi que une transformation affine qui transforme le premier visage au deuxième. Nous démontrons que notre formulation ne nécessite que la profondeur et que les paramètres affines peuvent être estimés avec un solution analytique impliquant les points clés augmentés par profondeur. Même en l'absence de supervision directe de la profondeur, la technique proposée extrait des valeurs de profondeur raisonnables qui diffèrent des vraies valeurs de profondeur par un facteur d'échelle et de décalage. Nous démontrons des applications d'estimation de profondeur pour la tâche de rotation de visage, ainsi que celle d'échange de visage.This thesis focuses on learning algorithms that extract important features from faces. The features of main interest are landmarks; the two dimensional (2D) or three dimensional (3D) locations of important facial features such as eye centers, nose tip, and mouth corners. Landmarks are used to solve complex tasks that cannot be solved directly or require guidance for enhanced performance, such as pose or gesture recognition, tracking, or face verification. The application of the models presented in this thesis is on facial images; however, the algorithms proposed are more general and can be applied to the landmarks of other forms of objects, such as hands, full body or man-made objects. This thesis is written by article and explores different techniques to solve various aspects of landmark localization. In the first article, we disentangle identity and expression of a given face to learn a prior distribution over the joint set of landmarks. This prior is then merged with a discriminative classifier that learns an independent probability distribution per landmark. The merged model is capable of explaining differences in expressions for the same identity representation. In the second article, we propose an architecture that aims at uncovering image features to do tasks that require high pixel-level accuracy, such as landmark localization or image segmentation. The proposed architecture gradually extracts coarser features in its encoding steps to get more global information over the image and then it expands the coarse features back to the image resolution by recombining the features of the encoding path. The model, termed Recombinator Networks, obtained state-of-the-art on several datasets, while also speeding up training. In the third article, we aim at improving landmark localization when only a few images with labelled landmarks are available. In particular, we leverage a weaker form of data labels that are easier to acquire or more abundantly available such as emotion or head pose. To do so, we propose an architecture to backpropagate gradients of the weaker labels through landmarks, effectively training the landmark localization network. We also propose an unsupervised loss component which makes equivariant landmark predictions with respect to transformations applied to the image without having ground truth landmark labels. These techniques improved performance considerably when we have a low percentage of labelled images with landmarks. Finally, in the last article, we propose a learning algorithm to estimate the depth of the landmarks without any depth supervision. We do so by matching landmarks of two faces through transforming one to another. This transformation requires estimation of depth on one face and an affine transformation that maps the first face to the second one. Our formulation, which only requires depth estimation and affine parameters, can be estimated as a closed form solution of the 2D landmarks and the estimated depth. Even without direct depth supervision, the proposed technique extracts reasonable depth values that differ from the ground truth depth values by a scale and a shift. We demonstrate applications of the estimated depth in face rotation and face replacement tasks

Design and Real-World Application of Novel Machine Learning Techniques for Improving Face Recognition Algorithms

Recent progress in machine learning has made possible the development of real-world face recognition applications that can match face images as good as or better than humans. However, several challenges remain unsolved. In this PhD thesis, some of these challenges are studied and novel machine learning techniques to improve the performance of real-world face recognition applications are proposed. Current face recognition algorithms based on deep learning techniques are able to achieve outstanding accuracy when dealing with face images taken in unconstrained environments. However, training these algorithms is often costly due to the very large datasets and the high computational resources needed. On the other hand, traditional methods for face recognition are better suited when these requirements cannot be satisfied. This PhD thesis presents new techniques for both traditional and deep learning methods. In particular, a novel traditional face recognition method that combines texture and shape features together with subspace representation techniques is first presented. The proposed method is lightweight and can be trained quickly with small datasets. This method is used for matching face images scanned from identity documents against face images stored in the biometric chip of such documents. Next, two new techniques to increase the performance of face recognition methods based on convolutional neural networks are presented. Specifically, a novel training strategy that increases face recognition accuracy when dealing with face images presenting occlusions, and a new loss function that improves the performance of the triplet loss function are proposed. Finally, the problem of collecting large face datasets is considered, and a novel method based on generative adversarial networks to synthesize both face images of existing subjects in a dataset and face images of new subjects is proposed. The accuracy of existing face recognition algorithms can be increased by training with datasets augmented with the synthetic face images generated by the proposed method. In addition to the main contributions, this thesis provides a comprehensive literature review of face recognition methods and their evolution over the years. A significant amount of the work presented in this PhD thesis is the outcome of a 3-year-long research project partially funded by Innovate UK as part of a Knowledge Transfer Partnership between University of Hertfordshire and IDscan Biometrics Ltd (partnership number: 009547)

Unsupervised Learning from Shollow to Deep

Machine learning plays a pivotal role in most state-of-the-art systems in many application research domains. With the rising of deep learning, massive labeled data become the solution of feature learning, which enables the model to learn automatically. Unfortunately, the trained deep learning model is hard to adapt to other datasets without fine-tuning, and the applicability of machine learning methods is limited by the amount of available labeled data. Therefore, the aim of this thesis is to alleviate the limitations of supervised learning by exploring algorithms to learn good internal representations, and invariant feature hierarchies from unlabelled data. Firstly, we extend the traditional dictionary learning and sparse coding algorithms onto hierarchical image representations in a principled way. To achieve dictionary atoms capture additional information from extended receptive fields and attain improved descriptive capacity, we present a two-pass multi-resolution cascade framework for dictionary learning and sparse coding. This cascade method allows collaborative reconstructions at different resolutions using only the same dimensional dictionary atoms. The jointly learned dictionary comprises atoms that adapt to the information available at the coarsest layer, where the support of atoms reaches a maximum range, and the residual images, where the supplementary details refine progressively a reconstruction objective. Our method generates flexible and accurate representations using only a small number of coefficients, and is efficient in computation. In the following work, we propose to incorporate the traditional self-expressiveness property into deep learning to explore better representation for subspace clustering. This architecture is built upon deep auto-encoders, which non-linearly map the input data into a latent space. Our key idea is to introduce a novel self-expressive layer between the encoder and the decoder to mimic the ``self-expressiveness'' property that has proven effective in traditional subspace clustering. Being differentiable, our new self-expressive layer provides a simple but effective way to learn pairwise affinities between all data points through a standard back-propagation procedure. Being nonlinear, our neural-network based method is able to cluster data points having complex (often nonlinear) structures. However, Subspace clustering algorithms are notorious for their scalability issues because building and processing large affinity matrices are demanding. We propose two methods to tackle this problem. One method is based on $k$-Subspace Clustering, where we introduce a method that simultaneously learns an embedding space along subspaces within it to minimize a notion of reconstruction error, thus addressing the problem of subspace clustering in an end-to-end learning paradigm. This in turn frees us from the need of having an affinity matrix to perform clustering. The other way starts from using a feed forward network to replace the spectral clustering and learn the affinities of each data from "self-expressive" layer. We introduce the Neural Collaborative Subspace Clustering, where it benefits from a classifier which determines whether a pair of points lies on the same subspace under supervision of "self-expressive" layer. Essential to our model is the construction of two affinity matrices, one from the classifier and the other from a notion of subspace self-expressiveness, to supervise training in a collaborative scheme. In summary, we make constributions on how to perform the unsupervised learning in several tasks in this thesis. It starts from traditional sparse coding and dictionary learning perspective in low-level vision. Then, we exploit how to incorporate unsupervised learning in convolutional neural networks without label information and make subspace clustering to large scale dataset. Furthermore, we also extend the clustering on dense prediction task (saliency detection)