Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.695528
Title: Robust subspace learning techniques for tracking and recognition of human faces
Author: Marras, Ioannis
ISNI:       0000 0004 5989 6067
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2015
Availability of Full Text:
Access through EThOS:
Full text unavailable from EThOS. Please try the link below.
Access through Institution:
Abstract:
Computer vision, in general, aims to duplicate (or in some cases compensate) human vision, and traditionally, have been used in performing routine, repetitive tasks, such as classification in massive assembly lines. Today, research on computer vision is spreading enormously so that it is almost impossible to itemize all of its subtopics. Despite of this fact, one can list relevant several applications, such as face processing (i.e. face, expression, and gesture recognition), computer human interaction, crowd surveillance, and content-based image retrieval. In this thesis, we propose subspace learning algorithms that head toward solving two important but largely understudied problems in automated face analysis: robust 2D plus 3D face tracking and robust 2D/3D face recognition in the wild. The methods that we propose for the former represent the pioneering work on face tracking and recognition. After describing all the unsolved problems a computer vision method for automated facial analysis has to deal with, we propose algorithms to deal with these problems. More specifically, we propose a subspace technique for robust rigid object tracking by fusing appearance models created based on different modalities. The proposed learning and fusing framework is robust, exact, computationally efficient and does not require off-line training. By using 3D information and an appropriate 3D motion model, pose and appearance are decoupled, and therefore learning and maintaining an updated model for appearance only is feasible by using efficient online subspace learning schemes, achieving in that way robust performance in very difficult tracking scenarios including extreme pose variations. Furthermore, we propose an efficient and robust subspace technique to gradient ascent automatic face recognition method which is based on a correlation-based approach to parametric object alignment. Our algorithm performs the face recognition task by registering two face images by iteratively maximizing their correlation coefficient using gradient ascent as well as an appropriate motion model. We show the robustness of our algorithm for the problem of face recognition in the presence of occlusions and non-uniform illumination changes. In addition, we introduce a simple, efficient and robust subspace-based method for learning from the azimuth angle of surface normals for 3D face recognition. We show that an efficient subspace-based data representation based on the normal azimuth angles can be used for robust face recognition from facial surfaces. We demonstrated some of the favourable properties of this framework for the application of 3D face recognition. Extensions of our scheme span a wide range of theoretical topics and applications, from statistical machine learning and clustering to 3D object recognition. An important aspect of this method is that it can achieve good face recognition/ verification performance by using raw 3D scans without any heavy preprocessing (i.e., model fitting, surface smoothing etc.). Finally, we propose a methodology that jointly learns a generative deformable model with minimal human intervention by using only a simple shape model of the object and images automatically downloaded from the Internet, and also extracts features appropriate for classification. The proposed algorithm is tested on various classification problems such as 'in-the-wild' face recognition, as well as, Internet image based vision applications such as gender classification and eye glasses detection on data collected automatically by querying into a web image search engine.
Supervisor: Pantic, Maja Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.695528  DOI: Not available
Share: