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

Robust face recognition

University of Technology Sydney. Faculty of Engineering and Information Technology.Face recognition is one of the most important and promising biometric techniques. In face recognition, a similarity score is automatically calculated between face images to further decide their identity. Due to its non-invasive characteristics and ease of use, it has shown great potential in many real-world applications, e.g., video surveillance, access control systems, forensics and security, and social networks. This thesis addresses key challenges inherent in real-world face recognition systems including pose and illumination variations, occlusion, and image blur. To tackle these challenges, a series of robust face recognition algorithms are proposed. These can be summarized as follows: In Chapter 2, we present a novel, manually designed face image descriptor named “Dual-Cross Patterns” (DCP). DCP efficiently encodes the seconder-order statistics of facial textures in the most informative directions within a face image. It proves to be more descriptive and discriminative than previous descriptors. We further extend DCP into a comprehensive face representation scheme named “Multi-Directional Multi-Level Dual-Cross Patterns” (MDML-DCPs). MDML-DCPs efficiently encodes the invariant characteristics of a face image from multiple levels into patterns that are highly discriminative of inter-personal differences but robust to intra-personal variations. MDML-DCPs achieves the best performance on the challenging FERET, FRGC 2.0, CAS-PEAL-R1, and LFW databases. In Chapter 3, we develop a deep learning-based face image descriptor named “Multimodal Deep Face Representation” (MM-DFR) to automatically learn face representations from multimodal image data. In brief, convolutional neural networks (CNNs) are designed to extract complementary information from the original holistic face image, the frontal pose image rendered by 3D modeling, and uniformly sampled image patches. The recognition ability of each CNN is optimized by carefully integrating a number of published or newly developed tricks. A feature level fusion approach using stacked auto-encoders is designed to fuse the features extracted from the set of CNNs, which is advantageous for non-linear dimension reduction. MM-DFR achieves over 99% recognition rate on LFW using publicly available training data. In Chapter 4, based on our research on handcrafted face image descriptors, we propose a powerful pose-invariant face recognition (PIFR) framework capable of handling the full range of pose variations within ±90° of yaw. The framework has two parts: the first is Patch-based Partial Representation (PBPR), and the second is Multi-task Feature Transformation Learning (MtFTL). PBPR transforms the original PIFR problem into a partial frontal face recognition problem. A robust patch-based face representation scheme is developed to represent the synthesized partial frontal faces. For each patch, a transformation dictionary is learnt under the MtFTL scheme. The transformation dictionary transforms the features of different poses into a discriminative subspace in which face matching is performed. The PBPR-MtFTL framework outperforms previous state-of-the-art PIFR methods on the FERET, CMU-PIE, and Multi-PIE databases. In Chapter 5, based on our research on deep learning-based face image descriptors, we design a novel framework named Trunk-Branch Ensemble CNN (TBE-CNN) to handle challenges in video-based face recognition (VFR) under surveillance circumstances. Three major challenges are considered: image blur, occlusion, and pose variation. First, to learn blur-robust face representations, we artificially blur training data composed of clear still images to account for a shortfall in real-world video training data. Second, to enhance the robustness of CNN features to pose variations and occlusion, we propose the TBE-CNN architecture, which efficiently extracts complementary information from holistic face images and patches cropped around facial components. Third, to further promote the discriminative power of the representations learnt by TBE-CNN, we propose an improved triplet loss function. With the proposed techniques, TBE-CNN achieves state-of-the-art performance on three popular video face databases: PaSC, COX Face, and YouTube Faces

Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation

Cross-resolution face recognition has become a challenging problem for modern deep face recognition systems. It aims at matching a low-resolution probe image with high-resolution gallery images registered in a database. Existing methods mainly leverage prior information from high-resolution images by either reconstructing facial details with super-resolution techniques or learning a unified feature space. To address this challenge, this paper proposes a new approach that enforces the network to focus on the discriminative information stored in the low-frequency components of a low-resolution image. A cross-resolution knowledge distillation paradigm is first employed as the learning framework. Then, an identity-preserving network, WaveResNet, and a wavelet similarity loss are designed to capture low-frequency details and boost performance. Finally, an image degradation model is conceived to simulate more realistic low-resolution training data. Consequently, extensive experimental results show that the proposed method consistently outperforms the baseline model and other state-of-the-art methods across a variety of image resolutions

Multi-View Graph Fusion for Semi-Supervised Learning: Application to Image-Based Face Beauty Prediction

Facial Beauty Prediction (FBP) is an important visual recognition problem to evaluate the attractiveness of faces according to human perception. Most existing FBP methods are based on supervised solutions using geometric or deep features. Semi-supervised learning for FBP is an almost unexplored research area. In this work, we propose a graph-based semi-supervised method in which multiple graphs are constructed to find the appropriate graph representation of the face images (with and without scores). The proposed method combines both geometric and deep feature-based graphs to produce a high-level representation of face images instead of using a single face descriptor and also improves the discriminative ability of graph-based score propagation methods. In addition to the data graph, our proposed approach fuses an additional graph adaptively built on the predicted beauty values. Experimental results on the SCUTFBP-5500 facial beauty dataset demonstrate the superiority of the proposed algorithm compared to other state-of-the-art methods

Unconstrained Face Verification using Deep CNN Features

In this paper, we present an algorithm for unconstrained face verification based on deep convolutional features and evaluate it on the newly released IARPA Janus Benchmark A (IJB-A) dataset. The IJB-A dataset includes real-world unconstrained faces from 500 subjects with full pose and illumination variations which are much harder than the traditional Labeled Face in the Wild (LFW) and Youtube Face (YTF) datasets. The deep convolutional neural network (DCNN) is trained using the CASIA-WebFace dataset. Extensive experiments on the IJB-A dataset are provided