7,300 research outputs found
Face Alignment in the Wild.
PhDFace 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.China Scholarship Counci
Occlusion Coherence: Detecting and Localizing Occluded Faces
The presence of occluders significantly impacts object recognition accuracy.
However, occlusion is typically treated as an unstructured source of noise and
explicit models for occluders have lagged behind those for object appearance
and shape. In this paper we describe a hierarchical deformable part model for
face detection and landmark localization that explicitly models part occlusion.
The proposed model structure makes it possible to augment positive training
data with large numbers of synthetically occluded instances. This allows us to
easily incorporate the statistics of occlusion patterns in a discriminatively
trained model. We test the model on several benchmarks for landmark
localization and detection including challenging new data sets featuring
significant occlusion. We find that the addition of an explicit occlusion model
yields a detection system that outperforms existing approaches for occluded
instances while maintaining competitive accuracy in detection and landmark
localization for unoccluded instances
Mirror, mirror on the wall, tell me, is the error small?
Do object part localization methods produce bilaterally symmetric results on
mirror images? Surprisingly not, even though state of the art methods augment
the training set with mirrored images. In this paper we take a closer look into
this issue. We first introduce the concept of mirrorability as the ability of a
model to produce symmetric results in mirrored images and introduce a
corresponding measure, namely the \textit{mirror error} that is defined as the
difference between the detection result on an image and the mirror of the
detection result on its mirror image. We evaluate the mirrorability of several
state of the art algorithms in two of the most intensively studied problems,
namely human pose estimation and face alignment. Our experiments lead to
several interesting findings: 1) Surprisingly, most of state of the art methods
struggle to preserve the mirror symmetry, despite the fact that they do have
very similar overall performance on the original and mirror images; 2) the low
mirrorability is not caused by training or testing sample bias - all algorithms
are trained on both the original images and their mirrored versions; 3) the
mirror error is strongly correlated to the localization/alignment error (with
correlation coefficients around 0.7). Since the mirror error is calculated
without knowledge of the ground truth, we show two interesting applications -
in the first it is used to guide the selection of difficult samples and in the
second to give feedback in a popular Cascaded Pose Regression method for face
alignment.Comment: 8 pages, 9 figure
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