Sparse and Deep Representations for Face Recognition and Object Detection

Face recognition and object detection are two very fundamental visual recognition applications in computer vision. How to learn “good” feature representations using machine learning has become the cornerstone of perception-based systems. A good feature representation is often the one that is robust and discriminative to multiple instances of the same category. Starting from features such as intensity, histogram etc. in the image, followed by hand-crafted features, to the most recent sophisticated deep feature representations, we have witnessed the remarkable improvement in the ability of a feature learning algorithm to perform pattern recognition tasks such as face recognition and object detection. One of the conventional feature learning methods, dictionary learning has been proposed to learn discriminative and sparse representations for visual recognition. These dictionary learning methods can learn both representative and discriminative dictionaries, and the associated sparse representations are effective for vision tasks such as face recognition. More recently, deep features have been widely adopted by the computer vision community owing to the powerful deep neural network, which is capable of distilling information from high dimensional input spaces to a low dimensional semantic space. The research problems which comprise this dissertation lie at the cross section of conventional feature and deep feature learning approaches. Thus, in this dissertation, we study both sparse and deep representations for face recognition and object detection. First, we begin by studying the topic of spare representations. We present a simple thresholded feature learning algorithm under sparse support recovery. We show that under certain conditions, the thresholded feature exactly recovers the nonzero support of the sparse code. Secondly, based on the theoretical guarantees, we derive the model and algorithm named Dictionary Learning for Thresholded Features (DLTF), to learn the dictionary that is optimized for the thresholded feature. The DLTF dictionaries are specifically designed for using the thresholded feature at inference, which prioritize simplicity, efficiency, general usability and theoretical guarantees. Both synthetic simulations and real-data experiments (i.e. image clustering and unsupervised hashing) verify the competitive quantitative results and remarkable efficiency of applying thresholded features with DLTF dictionaries. Continuing our focus on investigating the sparse representation and its application to computer vision tasks, we address the sparse representations for unconstrained face verification/recognition problem. In the first part, we address the video-based face recognition problem since it brings more challenges due to the fact that the videos are often acquired under significant variations in poses, expressions, lighting conditions and backgrounds. In order to extract representations that are robust to these variations, we propose a structured dictionary learning framework. Specifically, we employ dictionary learning and low-rank approximation methods to preserve the invariant structure of face images in videos. The learned structured dictionary is both discriminative and reconstructive. We demonstrate the effectiveness of our approach through extensive experiments on three video-based face recognition datasets. Recently, template-based face verification has gained more popularity. Unlike traditional verification tasks, which evaluate on image-to-image or video-to-video pairs, template-based face verification/recognition methods can exploit training and/or gallery data containing a mixture of both images or videos from the person of interest. In the second part, we propose a regularized sparse coding approach for template-based face verification. First, we construct a reference dictionary, which represents the training set. Then we learn the discriminative sparse codes of the templates for verification through the proposed template regularized sparse coding approach. Finally, we measure the similarity between templates. However, in real world scenarios, training and test data are sampled from different distributions. Therefore, we also extend the dictionary learning techniques to tackle the domain adaptation problem, where the data from the training set (source domain) and test set (target domain) have different underlying distributions (domain shift). We propose a domain-adaptive dictionary learning framework to model the domain shift by generating a set of intermediate domains. These intermediate domains bridge the gap between the source and target domains. Specifically, we not only learn a common dictionary to encode the domain-shared features but also learn a set of domain specific dictionaries to model the domain shift. This separation enables us to learn more compact and reconstructive dictionaries for domain adaptation. The domain-adaptive features for recognition are finally derived by aligning all the recovered feature representations of both source and target along the domain path. We evaluate our approach on both cross-domain face recognition and object classification tasks. Finally, we study another fundamental problem in computer vision: generic object detection. Object detection has become one of the most valuable pattern recognition tasks, with great benefits in scene understanding, face recognition, action recognition, robotics and self-driving vehicles, etc. We propose a novel object detector named "Deep Regionlets" by blending deep learning and the traditional regionlet method. The proposed framework "Deep Regionlets" is able to address the limitations of traditional regionlet methods, leading to significant precision improvement by exploiting the power of deep convolutional neural networks. Furthermore, we conduct a detailed analysis of our approach to understand its merits and properties. Extensive experiments on two detection benchmark datasets show that the proposed deep regionlet approach outperforms several state-of-the-art competitors

Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds

Sparsity-based representations have recently led to notable results in various visual recognition tasks. In a separate line of research, Riemannian manifolds have been shown useful for dealing with features and models that do not lie in Euclidean spaces. With the aim of building a bridge between the two realms, we address the problem of sparse coding and dictionary learning over the space of linear subspaces, which form Riemannian structures known as Grassmann manifolds. To this end, we propose to embed Grassmann manifolds into the space of symmetric matrices by an isometric mapping. This in turn enables us to extend two sparse coding schemes to Grassmann manifolds. Furthermore, we propose closed-form solutions for learning a Grassmann dictionary, atom by atom. Lastly, to handle non-linearity in data, we extend the proposed Grassmann sparse coding and dictionary learning algorithms through embedding into Hilbert spaces. Experiments on several classification tasks (gender recognition, gesture classification, scene analysis, face recognition, action recognition and dynamic texture classification) show that the proposed approaches achieve considerable improvements in discrimination accuracy, in comparison to state-of-the-art methods such as kernelized Affine Hull Method and graph-embedding Grassmann discriminant analysis.Comment: Appearing in International Journal of Computer Visio

Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective

This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly

Going Deeper into Action Recognition: A Survey

Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation. Over the last decade, human action analysis evolved from earlier schemes that are often limited to controlled environments to nowadays advanced solutions that can learn from millions of videos and apply to almost all daily activities. Given the broad range of applications from video surveillance to human-computer interaction, scientific milestones in action recognition are achieved more rapidly, eventually leading to the demise of what used to be good in a short time. This motivated us to provide a comprehensive review of the notable steps taken towards recognizing human actions. To this end, we start our discussion with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches. We aim to remain objective throughout this survey, touching upon encouraging improvements as well as inevitable fallbacks, in the hope of raising fresh questions and motivating new research directions for the reader