From 3D Point Clouds to Pose-Normalised Depth Maps

We consider the problem of generating either pairwise-aligned or pose-normalised depth maps from noisy 3D point clouds in a relatively unrestricted poses. Our system is deployed in a 3D face alignment application and consists of the following four stages: (i) data filtering, (ii) nose tip identification and sub-vertex localisation, (iii) computation of the (relative) face orientation, (iv) generation of either a pose aligned or a pose normalised depth map. We generate an implicit radial basis function (RBF) model of the facial surface and this is employed within all four stages of the process. For example, in stage (ii), construction of novel invariant features is based on sampling this RBF over a set of concentric spheres to give a spherically-sampled RBF (SSR) shape histogram. In stage (iii), a second novel descriptor, called an isoradius contour curvature signal, is defined, which allows rotational alignment to be determined using a simple process of 1D correlation. We test our system on both the University of York (UoY) 3D face dataset and the Face Recognition Grand Challenge (FRGC) 3D data. For the more challenging UoY data, our SSR descriptors significantly outperform three variants of spin images, successfully identifying nose vertices at a rate of 99.6%. Nose localisation performance on the higher quality FRGC data, which has only small pose variations, is 99.9%. Our best system successfully normalises the pose of 3D faces at rates of 99.1% (UoY data) and 99.6% (FRGC data)

3D FACE RECOGNITION USING LOCAL FEATURE BASED METHODS

Face recognition has attracted many researchers’ attention compared to other biometrics due to its non-intrusive and friendly nature. Although several methods for 2D face recognition have been proposed so far, there are still some challenges related to the 2D face including illumination, pose variation, and facial expression. In the last few decades, 3D face research area has become more interesting since shape and geometry information are used to handle challenges from 2D faces. Existing algorithms for face recognition are divided into three different categories: holistic feature-based, local feature-based, and hybrid methods. According to the literature, local features have shown better performance relative to holistic feature-based methods under expression and occlusion challenges. In this dissertation, local feature-based methods for 3D face recognition have been studied and surveyed. In the survey, local methods are classified into three broad categories which consist of keypoint-based, curve-based, and local surface-based methods. Inspired by keypoint-based methods which are effective to handle partial occlusion, structural context descriptor on pyramidal shape maps and texture image has been proposed in a multimodal scheme. Score-level fusion is used to combine keypoints’ matching score in both texture and shape modalities. The survey shows local surface-based methods are efficient to handle facial expression. Accordingly, a local derivative pattern is introduced to extract distinct features from depth map in this work. In addition, the local derivative pattern is applied on surface normals. Most 3D face recognition algorithms are focused to utilize the depth information to detect and extract features. Compared to depth maps, surface normals of each point can determine the facial surface orientation, which provides an efficient facial surface representation to extract distinct features for recognition task. An Extreme Learning Machine (ELM)-based auto-encoder is used to make the feature space more discriminative. Expression and occlusion robust analysis using the information from the normal maps are investigated by dividing the facial region into patches. A novel hybrid classifier is proposed to combine Sparse Representation Classifier (SRC) and ELM classifier in a weighted scheme. The proposed algorithms have been evaluated on four widely used 3D face databases; FRGC, Bosphorus, Bu-3DFE, and 3D-TEC. The experimental results illustrate the effectiveness of the proposed approaches. The main contribution of this work lies in identification and analysis of effective local features and a classification method for improving 3D face recognition performance

Feature extraction for range image interpretation using local topology statistics

This thesis presents an approach for interpreting range images of known subject matter, such as the human face, based on the extraction and matching of local features from the images. In recent years, approaches to interpret two-dimensional (2D) images based on local feature extraction have advanced greatly, for example, systems such as Scale Invariant Feature Transform (SIFT) can detect and describe the local features in the 2D images effectively. With the aid of rapidly advancing three-dimensional (3D) imaging technology, in particular, the advent of commercially available surface scanning systems based on photogrammetry, image representation has been able to extend into the third dimension. Moreover, range images confer a number of advantages over conventional 2D images, for instance, the properties of being invariant to lighting, pose and viewpoint changes. As a result, an attempt has been made in this work to establish how best to represent the local range surface with a feature descriptor, thereby developing a matching system that takes advantages of the third dimension present in the range images and casting this in the framework of an existing scale and rotational invariance recognition technology: SIFT. By exploring the statistical representations of the local variation, it is possible to represent and match range images of human faces. This can be achieved by extracting unique mathematical keys known as feature descriptors, from the various automatically generated stable keypoint locations of the range images, thereby capturing the local information of the distributions of the mixes of surface types and their orientations simultaneously. Keypoints are generated through scale-space approach, where the (x,y) location and the appropriate scale (sigma) are detected. In order to achieve invariance to in-plane viewpoint rotational changes, a consistent canonical orientation is assigned to each keypoint and the sampling patch is rotated to this canonical orientation. The mixes of surface types, derived using the shape index, and the image gradient orientations are extracted from each sampling patch by placing nine overlapping Gaussian sub-regions over the measurement aperture. Each of the nine regions is overlapped by one standard deviation in order to minimise the occurrence of spatial aliasing during the sampling stages and to provide a better continuity within the descriptor. Moreover, surface normals can be computed from each of the keypoint location, allowing the local 3D pose to be estimated and corrected within the feature descriptors since the orientations in which the images were captured are unknown a priori. As a result, the formulated feature descriptors have strong discriminative power and are stable to rotational changes

Facial Texture Super-Resolution by Fitting 3D Face Models

This book proposes to solve the low-resolution (LR) facial analysis problem with 3D face super-resolution (FSR). A complete processing chain is presented towards effective 3D FSR in real world. To deal with the extreme challenges of incorporating 3D modeling under the ill-posed LR condition, a novel workflow coupling automatic localization of 2D facial feature points and 3D shape reconstruction is developed, leading to a robust pipeline for pose-invariant hallucination of the 3D facial texture

Shape classification: towards a mathematical description of the face

Recent advances in biostereometric techniques have led to the quick and easy acquisition of 3D data for facial and other biological surfaces. This has led facial surgeons to express dissatisfaction with landmark-based methods for analysing the shape of the face which use only a small part of the data available, and to seek a method for analysing the face which maximizes the use of this extensive data set. Scientists working in the field of computer vision have developed a variety of methods for the analysis and description of 2D and 3D shape. These methods are reviewed and an approach, based on differential geometry, is selected for the description of facial shape. For each data point, the Gaussian and mean curvatures of the surface are calculated. The performance of three algorithms for computing these curvatures are evaluated for mathematically generated standard 3D objects and for 3D data obtained from an optical surface scanner. Using the signs of these curvatures, the face is classified into eight 'fundamental surface types' - each of which has an intuitive perceptual meaning. The robustness of the resulting surface type description to errors in the data is determined together with its repeatability. Three methods for comparing two surface type descriptions are presented and illustrated for average male and average female faces. Thus a quantitative description of facial change, or differences between individual's faces, is achieved. The possible application of artificial intelligence techniques to automate this comparison is discussed. The sensitivity of the description to global and local changes to the data, made by mathematical functions, is investigated. Examples are given of the application of this method for describing facial changes made by facial reconstructive surgery and implications for defining a basis for facial aesthetics using shape are discussed. It is also applied to investigate the role played by the shape of the surface in facial recognition

Landmark Localization, Feature Matching and Biomarker Discovery from Magnetic Resonance Images

The work presented in this thesis proposes several methods that can be roughly divided into three different categories: I) landmark localization in medical images, II) feature matching for image registration, and III) biomarker discovery in neuroimaging. The first part deals with the identification of anatomical landmarks. The motivation stems from the fact that the manual identification and labeling of these landmarks is very time consuming and prone to observer errors, especially when large datasets must be analyzed. In this thesis we present three methods to tackle this challenge: A landmark descriptor based on local self-similarities (SS), a subspace building framework based on manifold learning and a sparse coding landmark descriptor based on data-specific learned dictionary basis. The second part of this thesis deals with finding matching features between a pair of images. These matches can be used to perform a registration between them. Registration is a powerful tool that allows mapping images in a common space in order to aid in their analysis. Accurate registration can be challenging to achieve using intensity based registration algorithms. Here, a framework is proposed for learning correspondences in pairs of images by matching SS features and random sample and consensus (RANSAC) is employed as a robust model estimator to learn a deformation model based on feature matches. Finally, the third part of the thesis deals with biomarker discovery using machine learning. In this section a framework for feature extraction from learned low-dimensional subspaces that represent inter-subject variability is proposed. The manifold subspace is built using data-driven regions of interest (ROI). These regions are learned via sparse regression, with stability selection. Also, probabilistic distribution models for different stages in the disease trajectory are estimated for different class populations in the low-dimensional manifold and used to construct a probabilistic scoring function.Open Acces

View Synthesis from Image and Video for Object Recognition Applications

Object recognition is one of the most important and successful applications in computer vision community. The varying appearances of the test object due to different poses or illumination conditions can make the object recognition problem very challenging. Using view synthesis techniques to generate pose-invariant or illumination-invariant images or videos of the test object is an appealing approach to alleviate the degrading recognition performance due to non-canonical views or lighting conditions. In this thesis, we first present a complete framework for better synthesis and understanding of the human pose from a limited number of available silhouette images. Pose-normalized silhouette images are generated using an active virtual camera and an image based visual hull technique, with the silhouette turning function distance being used as the pose similarity measurement. In order to overcome the inability of the shape from silhouettes method to reonstruct concave regions for human postures, a view synthesis algorithm is proposed for articulating humans using visual hull and contour-based body part segmentation. These two components improve each other for better performance through the correspondence across viewpoints built via the inner distance shape context measurement. Face recognition under varying pose is a challenging problem, especially when illumination variations are also present. We propose two algorithms to address this scenario. For a single light source, we demonstrate a pose-normalized face synthesis approach on a pixel-by-pixel basis from a single view by exploiting the bilateral symmetry of the human face. For more complicated illumination condition, the spherical harmonic representation is extended to encode pose information. An efficient method is proposed for robust face synthesis and recognition with a very compact training set. Finally, we present an end-to-end moving object verification system for airborne video, wherein a homography based view synthesis algorithm is used to simultaneously handle the object's changes in aspect angle, depression angle, and resolution. Efficient integration of spatial and temporal model matching assures the robustness of the verification step. As a byproduct, a robust two camera tracking method using homography is also proposed and demonstrated using challenging surveillance video sequences