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

Multimodal Biometrics for Robust Fusion Systems using Logic Gates

Many professionals indicate that unimodal biometric recognition systems have many shortcomings associated with performance accuracy rates. In order to make the system design more robust, we propose a multimodal biometric which includes fingerprint and face recognition using logical AND operators at decision-level fusion. In this paper, we also discuss some concerns about the security issues regarding the identification and verification processes for the multimodal recognition system against invaders and threatening attackers. While the unimodal fingerprint and face biometric gives recognition rate of 94% and 90.8% respectively, the multi-modal approach was giving a recognition rate of 98% at the decision level fusion, showing an improvement in the accuracy. Also, both the FAR and FRR have been considerably reduced, showing that the multi-modal system implemented is more robust

Face Liveness Detection under Processed Image Attacks

Face recognition is a mature and reliable technology for identifying people. Due to high-definition cameras and supporting devices, it is considered the fastest and the least intrusive biometric recognition modality. Nevertheless, effective spoofing attempts on face recognition systems were found to be possible. As a result, various anti-spoofing algorithms were developed to counteract these attacks. They are commonly referred in the literature a liveness detection tests. In this research we highlight the effectiveness of some simple, direct spoofing attacks, and test one of the current robust liveness detection algorithms, i.e. the logistic regression based face liveness detection from a single image, proposed by the Tan et al. in 2010, against malicious attacks using processed imposter images. In particular, we study experimentally the effect of common image processing operations such as sharpening and smoothing, as well as corruption with salt and pepper noise, on the face liveness detection algorithm, and we find that it is especially vulnerable against spoofing attempts using processed imposter images. We design and present a new facial database, the Durham Face Database, which is the first, to the best of our knowledge, to have client, imposter as well as processed imposter images. Finally, we evaluate our claim on the effectiveness of proposed imposter image attacks using transfer learning on Convolutional Neural Networks. We verify that such attacks are more difficult to detect even when using high-end, expensive machine learning techniques

Deep Learning Architectures for Heterogeneous Face Recognition

Face recognition has been one of the most challenging areas of research in biometrics and computer vision. Many face recognition algorithms are designed to address illumination and pose problems for visible face images. In recent years, there has been significant amount of research in Heterogeneous Face Recognition (HFR). The large modality gap between faces captured in different spectrum as well as lack of training data makes heterogeneous face recognition (HFR) quite a challenging problem. In this work, we present different deep learning frameworks to address the problem of matching non-visible face photos against a gallery of visible faces. Algorithms for thermal-to-visible face recognition can be categorized as cross-spectrum feature-based methods, or cross-spectrum image synthesis methods. In cross-spectrum feature-based face recognition a thermal probe is matched against a gallery of visible faces corresponding to the real-world scenario, in a feature subspace. The second category synthesizes a visible-like image from a thermal image which can then be used by any commercial visible spectrum face recognition system. These methods also beneficial in the sense that the synthesized visible face image can be directly utilized by existing face recognition systems which operate only on the visible face imagery. Therefore, using this approach one can leverage the existing commercial-off-the-shelf (COTS) and government-off-the-shelf (GOTS) solutions. In addition, the synthesized images can be used by human examiners for different purposes. There are some informative traits, such as age, gender, ethnicity, race, and hair color, which are not distinctive enough for the sake of recognition, but still can act as complementary information to other primary information, such as face and fingerprint. These traits, which are known as soft biometrics, can improve recognition algorithms while they are much cheaper and faster to acquire. They can be directly used in a unimodal system for some applications. Usually, soft biometric traits have been utilized jointly with hard biometrics (face photo) for different tasks in the sense that they are considered to be available both during the training and testing phases. In our approaches we look at this problem in a different way. We consider the case when soft biometric information does not exist during the testing phase, and our method can predict them directly in a multi-tasking paradigm. There are situations in which training data might come equipped with additional information that can be modeled as an auxiliary view of the data, and that unfortunately is not available during testing. This is the LUPI scenario. We introduce a novel framework based on deep learning techniques that leverages the auxiliary view to improve the performance of recognition system. We do so by introducing a formulation that is general, in the sense that can be used with any visual classifier. Every use of auxiliary information has been validated extensively using publicly available benchmark datasets, and several new state-of-the-art accuracy performance values have been set. Examples of application domains include visual object recognition from RGB images and from depth data, handwritten digit recognition, and gesture recognition from video. We also design a novel aggregation framework which optimizes the landmark locations directly using only one image without requiring any extra prior which leads to robust alignment given arbitrary face deformations. Three different approaches are employed to generate the manipulated faces and two of them perform the manipulation via the adversarial attacks to fool a face recognizer. This step can decouple from our framework and potentially used to enhance other landmark detectors. Aggregation of the manipulated faces in different branches of proposed method leads to robust landmark detection. Finally we focus on the generative adversarial networks which is a very powerful tool in synthesizing a visible-like images from the non-visible images. The main goal of a generative model is to approximate the true data distribution which is not known. In general, the choice for modeling the density function is challenging. Explicit models have the advantage of explicitly calculating the probability densities. There are two well-known implicit approaches, namely the Generative Adversarial Network (GAN) and Variational AutoEncoder (VAE) which try to model the data distribution implicitly. The VAEs try to maximize the data likelihood lower bound, while a GAN performs a minimax game between two players during its optimization. GANs overlook the explicit data density characteristics which leads to undesirable quantitative evaluations and mode collapse. This causes the generator to create similar looking images with poor diversity of samples. In the last chapter of thesis, we focus to address this issue in GANs framework

Privacy-Preserving Facial Recognition Using Biometric-Capsules

Indiana University-Purdue University Indianapolis (IUPUI)In recent years, developers have used the proliferation of biometric sensors in smart devices, along with recent advances in deep learning, to implement an array of biometrics-based recognition systems. Though these systems demonstrate remarkable performance and have seen wide acceptance, they present unique and pressing security and privacy concerns. One proposed method which addresses these concerns is the elegant, fusion-based Biometric-Capsule (BC) scheme. The BC scheme is provably secure, privacy-preserving, cancellable and interoperable in its secure feature fusion design. In this work, we demonstrate that the BC scheme is uniquely fit to secure state-of-the-art facial verification, authentication and identification systems. We compare the performance of unsecured, underlying biometrics systems to the performance of the BC-embedded systems in order to directly demonstrate the minimal effects of the privacy-preserving BC scheme on underlying system performance. Notably, we demonstrate that, when seamlessly embedded into a state-of-the-art FaceNet and ArcFace verification systems which achieve accuracies of 97.18% and 99.75% on the benchmark LFW dataset, the BC-embedded systems are able to achieve accuracies of 95.13% and 99.13% respectively. Furthermore, we also demonstrate that the BC scheme outperforms or performs as well as several other proposed secure biometric methods

On Intelligent Surveillance Systems and Face Recognition for Mass Transport Security

We describe a project to trial and develop enhanced surveillance technologies for public safety. A key technology is robust recognition of faces from low-resolution CCTV footage where there may be as few as 12 pixels between the eyes. Current commercial face recognition systems require 60-90 pixels between the eyes as well as tightly controlled image capture conditions. Our group has thus concentrated on fundamental face recognition issues such as robustness to low resolution and image capture conditions as required for uncontrolled CCTV surveillance. In this paper, we propose a fast multi-class pattern classification approach to enhance PCA and FLD methods for 2D face recognition under changes in pose, illumination, and expression. The method first finds the optimal weights of features pairwise and constructs a feature chain in order to determine the weights for all features. Computational load of the proposed approach is extremely low by design, in order to facilitate usage in automated surveillance. The method is evaluated on PIE, FERET, and Asian Face databases, with the results showing that the method performs remarkably well compared to several benchmark appearance-based methods. Moreover, the method can reliably recognise faces with large pose angles from just one gallery image