Face Alignment in the Wild.
Abstract
Face alignment on a face image is a crucial step in many computer vision applications such
as face recognition, verification and facial expression recognition. In this thesis we present
a collection of methods for face alignment in real-world scenarios where the acquisition
of the face images cannot be controlled. We first investigate local based random regression
forest methods that work in a voting fashion. We focus on building better quality
random trees, first, by using privileged information and second, in contrast to using explicit
shape models, by incorporating spatial shape constraints within the forests. We also
propose a fine-tuning scheme that sieves and/or aggregates regression forest votes before
accumulating them into the Hough space. We then investigate holistic methods and propose
two schemes, namely the cascaded regression forests and the random subspace supervised
descent method (RSSDM). The former uses a regression forest as the primitive regressor
instead of random ferns and an intelligent initialization scheme. The RSSDM improves the
accuracy and generalization capacity of the popular SDM by using several linear regressions
in random subspaces. We also propose a Cascaded Pose Regression framework for
face alignment in different modalities, that is RGB and sketch images, based on a sketch
synthesis scheme. Finally, we introduce the concept of mirrorability which describes how
an object alignment method behaves on mirror images in comparison to how it behaves on
the original ones. We define a measure called mirror error to quantitatively analyse the mirrorability
and show two applications, namely difficult samples selection and cascaded face
alignment feedback that aids a re-initialisation scheme. The methods proposed in this thesis
perform better or comparable to state of the art methods. We also demonstrate the generality
by applying them on similar problems such as car alignment.
Authors
Yang, HengCollections
- Theses [4504]