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.