Side-View Face Recognition

Side-view face recognition is a challenging problem with many applications. Especially in real-life scenarios where the environment is uncontrolled, coping with pose variations up to side-view positions is an important task for face recognition. In this paper we discuss the use of side view face recognition techniques to be used in house safety applications. Our aim is to recognize people as they pass through a door, and estimate their location in the house. Here, we compare available databases appropriate for this task, and review current methods for profile face recognition

Learning-based face synthesis for pose-robust recognition from single image

Face recognition in real-world conditions requires the ability to deal with a number of conditions, such as variations in pose, illumination and expression. In this paper, we focus on variations in head pose and use a computationally efficient regression-based approach for synthesising face images in different poses, which are used to extend the face recognition training set. In this data-driven approach, the correspondences between facial landmark points in frontal and non-frontal views are learnt offline from manually annotated training data via Gaussian Process Regression. We then use this learner to synthesise non-frontal face images from any unseen frontal image. To demonstrate the utility of this approach, two frontal face recognition systems (the commonly used PCA and the recent Multi-Region Histograms) are augmented with synthesised non-frontal views for each person. This synthesis and augmentation approach is experimentally validated on the FERET dataset, showing a considerable improvement in recognition rates for ±40◦ and ±60◦ views, while maintaining high recognition rates for ±15◦ and ±25◦ views

Unsupervised Face Alignment by Robust Nonrigid Mapping

We propose a novel approach to unsupervised facial im-age alignment. Differently from previous approaches, that are confined to affine transformations on either the entire face or separate patches, we extract a nonrigid mapping be-tween facial images. Based on a regularized face model, we frame unsupervised face alignment into the Lucas-Kanade image registration approach. We propose a robust optimiza-tion scheme to handle appearance variations. The method is fully automatic and can cope with pose variations and ex-pressions, all in an unsupervised manner. Experiments on a large set of images showed that the approach is effective. 1

Dense Wide-Baseline Stereo with Varying Illumination and its Application to Face Recognition

We study the problem of dense wide baseline stereo with varying illumination. We are motivated by the problem of face recognition across pose. Stereo matching allows us to compare face images based on physically valid, dense correspondences. We show that the stereo matching cost provides a very robust measure of the similarity of faces that is insensitive to pose variations. We build on the observation that most illumination insensitive local comparisons require the use of relatively large windows. The size of these windows is affected by foreshortening. If we do not account for this effect, we incur misalignments that are systematic and significant and are exacerbated by wide baseline conditions. We present a general formulation of dense wide baseline stereo with varying illumination and provide two methods to solve them. The first method is based on dynamic programming (DP) and fully accounts for the effect of slant. The second method is based on graph cuts (GC) and fully accounts for the effect of both slant and tilt. The GC method finds a global solution using the unary function from the general formulation and a novel smoothness term that encodes surface orientation. Our experiments show that DP dense wide baseline stereo achieves superior performance compared to existing methods in face recognition across pose. The experiments with the GC method show that accounting for both slant and tilt can improve performance in situations with wide baselines and lighting variation. Our formulation can be applied to other more sophisticated window based image comparison methods for stereo

A PCA approach to the object constancy for faces using view-based models of the face

The analysis of object and face recognition by humans attracts a great deal of interest, mainly because of its many applications in various fields, including psychology, security, computer technology, medicine and computer graphics. The aim of this work is to investigate whether a PCA-based mapping approach can offer a new perspective on models of object constancy for faces in human vision. An existing system for facial motion capture and animation developed for performance-driven animation of avatars is adapted, improved and repurposed to study face representation in the context of viewpoint and lighting invariance. The main goal of the thesis is to develop and evaluate a new approach to viewpoint invariance that is view-based and allows mapping of facial variation between different views to construct a multi-view representation of the face. The thesis describes a computer implementation of a model that uses PCA to generate example- based models of the face. The work explores the joint encoding of expression and viewpoint using PCA and the mapping between viewspecific PCA spaces. The simultaneous, synchronised video recording of 6 views of the face was used to construct multi-view representations, which helped to investigate how well multiple views could be recovered from a single view via the content addressable memory property of PCA. A similar approach was taken to lighting invariance. Finally, the possibility of constructing a multi-view representation from asynchronous view-based data was explored. The results of this thesis have implications for a continuing research problem in computer vision – the problem of recognising faces and objects from different perspectives and in different lighting. It also provides a new approach to understanding viewpoint invariance and lighting invariance in human observers

Fundamental Approaches to Software Engineering

This open access book constitutes the proceedings of the 25th International Conference on Fundamental Approaches to Software Engineering, FASE 2022, which was held during April 4-5, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 17 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. The proceedings also contain 3 contributions from the Test-Comp Competition. The papers deal with the foundations on which software engineering is built, including topics like software engineering as an engineering discipline, requirements engineering, software architectures, software quality, model-driven development, software processes, software evolution, AI-based software engineering, and the specification, design, and implementation of particular classes of systems, such as (self-)adaptive, collaborative, AI, embedded, distributed, mobile, pervasive, cyber-physical, or service-oriented applications

Fundamental Approaches to Software Engineering

This open access book constitutes the proceedings of the 25th International Conference on Fundamental Approaches to Software Engineering, FASE 2022, which was held during April 4-5, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 17 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. The proceedings also contain 3 contributions from the Test-Comp Competition. The papers deal with the foundations on which software engineering is built, including topics like software engineering as an engineering discipline, requirements engineering, software architectures, software quality, model-driven development, software processes, software evolution, AI-based software engineering, and the specification, design, and implementation of particular classes of systems, such as (self-)adaptive, collaborative, AI, embedded, distributed, mobile, pervasive, cyber-physical, or service-oriented applications

A Viewpoint Invariant, Sparsely Registered, Patch Based, Face Verifier

Sparsely registering a face (i.e., locating 2-3 fiducial points) is considered a much easier task than densely registering one; especially with varying viewpoints. Unfortunately, the converse tends to be true for the task of viewpoint-invariant face verification; the more registration points one has the better the performance. In this paper we present a novel approach to viewpoint invariant face verification which we refer to as the “patch-whole ” algorithm. The algorithm is able to obtain good verification performance with sparsely registered faces. Good performance is achieved by not as-suming any alignment between gallery and probe view faces, but instead tries to learn the joint likelihood functions for faces of similar and dissimilar identities. Generalization is encouraged by factorizing the joint gallery and probe appearance likelihood, for each class, into an ensemble of “patch-whole ” likelihoods. We make an additional contribution in this paper by reviewing existing approaches to viewpoint-invariant face verification and demonstrating how most of them fall into one of two categories; namely viewpoint-generative or viewpoint-discriminative. This categoriza-tion is instructive as it enables us to compare our “patch-whole ” algorithm to other paradigms in viewpoint-invariant face verification and also gives deeper insights into why the algorithm per-forms so well

A viewpoint invariant, sparsely registered, patch based, face verifier

10.1007/s11263-007-0119-zInternational Journal of Computer Vision80158-71IJCV