Sparse and Deep Representations for Face Recognition and Object Detection

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2019

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Abstract

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.

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