Automatic Face Recognition System Based on Local Fourier-Bessel Features

We present an automatic face verification system inspired by known properties of biological systems. In the proposed algorithm the whole image is converted from the spatial to polar frequency domain by a Fourier-Bessel Transform (FBT). Using the whole image is compared to the case where only face image regions (local analysis) are considered. The resulting representations are embedded in a dissimilarity space, where each image is represented by its distance to all the other images, and a Pseudo-Fisher discriminator is built. Verification test results on the FERET database showed that the local-based algorithm outperforms the global-FBT version. The local-FBT algorithm performed as state-of-the-art methods under different testing conditions, indicating that the proposed system is highly robust for expression, age, and illumination variations. We also evaluated the performance of the proposed system under strong occlusion conditions and found that it is highly robust for up to 50% of face occlusion. Finally, we automated completely the verification system by implementing face and eye detection algorithms. Under this condition, the local approach was only slightly superior to the global approach.Comment: 2005, Brazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI

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

Subspace Representations for Robust Face and Facial Expression Recognition

Analyzing human faces and modeling their variations have always been of interest to the computer vision community. Face analysis based on 2D intensity images is a challenging problem, complicated by variations in pose, lighting, blur, and non-rigid facial deformations due to facial expressions. Among the different sources of variation, facial expressions are of interest as important channels of non-verbal communication. Facial expression analysis is also affected by changes in view-point and inter-subject variations in performing different expressions. This dissertation makes an attempt to address some of the challenges involved in developing robust algorithms for face and facial expression recognition by exploiting the idea of proper subspace representations for data. Variations in the visual appearance of an object mostly arise due to changes in illumination and pose. So we first present a video-based sequential algorithm for estimating the face albedo as an illumination-insensitive signature for face recognition. We show that by knowing/estimating the pose of the face at each frame of a sequence, the albedo can be efficiently estimated using a Kalman filter. Then we extend this to the case of unknown pose by simultaneously tracking the pose as well as updating the albedo through an efficient Bayesian inference method performed using a Rao-Blackwellized particle filter. Since understanding the effects of blur, especially motion blur, is an important problem in unconstrained visual analysis, we then propose a blur-robust recognition algorithm for faces with spatially varying blur. We model a blurred face as a weighted average of geometrically transformed instances of its clean face. We then build a matrix, for each gallery face, whose column space spans the space of all the motion blurred images obtained from the clean face. This matrix representation is then used to define a proper objective function and perform blur-robust face recognition. To develop robust and generalizable models for expression analysis one needs to break the dependence of the models on the choice of the coordinate frame of the camera. To this end, we build models for expressions on the affine shape-space (Grassmann manifold), as an approximation to the projective shape-space, by using a Riemannian interpretation of deformations that facial expressions cause on different parts of the face. This representation enables us to perform various expression analysis and recognition algorithms without the need for pose normalization as a preprocessing step. There is a large degree of inter-subject variations in performing various expressions. This poses an important challenge on developing robust facial expression recognition algorithms. To address this challenge, we propose a dictionary-based approach for facial expression analysis by decomposing expressions in terms of action units (AUs). First, we construct an AU-dictionary using domain experts' knowledge of AUs. To incorporate the high-level knowledge regarding expression decomposition and AUs, we then perform structure-preserving sparse coding by imposing two layers of grouping over AU-dictionary atoms as well as over the test image matrix columns. We use the computed sparse code matrix for each expressive face to perform expression decomposition and recognition. Most of the existing methods for the recognition of faces and expressions consider either the expression-invariant face recognition problem or the identity-independent facial expression recognition problem. We propose joint face and facial expression recognition using a dictionary-based component separation algorithm (DCS). In this approach, the given expressive face is viewed as a superposition of a neutral face component with a facial expression component, which is sparse with respect to the whole image. This assumption leads to a dictionary-based component separation algorithm, which benefits from the idea of sparsity and morphological diversity. The DCS algorithm uses the data-driven dictionaries to decompose an expressive test face into its constituent components. The sparse codes we obtain as a result of this decomposition are then used for joint face and expression recognition

Information hiding through variance of the parametric orientation underlying a B-rep face

Watermarking technologies have been proposed for many different,types of digital media. However, to this date, no viable watermarking techniques have yet emerged for the high value B-rep (i.e. Boundary Representation) models used in 3D mechanical CAD systems. In this paper, the authors propose a new approach (PO-Watermarking) that subtly changes a model's geometric representation to incorporate a 'transparent' signature. This scheme enables software applications to create fragile, or robust watermarks without changing the size of the file, or shape of the CAD model. Also discussed is the amount of information the proposed method could transparently embed into a B-rep model. The results presented demonstrate the embedding and retrieval of text strings and investigate the robustness of the approach after a variety of transformation and modifications have been carried out on the data