Constructing Structures of Facial Identities on the View Sphere Using
Kernel Discriminant Analysis
Yongmin Li, Shaogang Gong and Heather Liddell
University of London
We present a novel approach to face recognition by constructing facial
identity structures across views and over time, referred to as identity
surfaces, in a Kernel Discriminant Analysis (KDA) feature space. This
approach is aimed at addressing three challenging problems in face
recognition: extracting the non-linear discriminant features,
recognising faces across multiple views, and recognising moving faces over
time. First, the KDA is developed to compute the most significant non-linear
basis vectors with the intention of maximising the between-class variance
and minimising the within-class variance. We applied KDA to the problem of
multi-view face recognition, and a
significant improvement has been achieved in robustness and accuracy.
Second, identity surfaces are constructed to model the variance of facial
appearance caused from rotation in depth. Recognition can then be
conveniently performed by computing the pattern distances from the identity
surfaces. Third, video-based online face recognition is performed by
computing and matching object onto identity surfaces which encode the
spatio-temporal dynamics
of moving faces.