19,606 research outputs found
Face Alignment Assisted by Head Pose Estimation
In this paper we propose a supervised initialization scheme for cascaded face
alignment based on explicit head pose estimation. We first investigate the
failure cases of most state of the art face alignment approaches and observe
that these failures often share one common global property, i.e. the head pose
variation is usually large. Inspired by this, we propose a deep convolutional
network model for reliable and accurate head pose estimation. Instead of using
a mean face shape, or randomly selected shapes for cascaded face alignment
initialisation, we propose two schemes for generating initialisation: the first
one relies on projecting a mean 3D face shape (represented by 3D facial
landmarks) onto 2D image under the estimated head pose; the second one searches
nearest neighbour shapes from the training set according to head pose distance.
By doing so, the initialisation gets closer to the actual shape, which enhances
the possibility of convergence and in turn improves the face alignment
performance. We demonstrate the proposed method on the benchmark 300W dataset
and show very competitive performance in both head pose estimation and face
alignment.Comment: Accepted by BMVC201
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
Simultaneous Facial Landmark Detection, Pose and Deformation Estimation under Facial Occlusion
Facial landmark detection, head pose estimation, and facial deformation
analysis are typical facial behavior analysis tasks in computer vision. The
existing methods usually perform each task independently and sequentially,
ignoring their interactions. To tackle this problem, we propose a unified
framework for simultaneous facial landmark detection, head pose estimation, and
facial deformation analysis, and the proposed model is robust to facial
occlusion. Following a cascade procedure augmented with model-based head pose
estimation, we iteratively update the facial landmark locations, facial
occlusion, head pose and facial de- formation until convergence. The
experimental results on benchmark databases demonstrate the effectiveness of
the proposed method for simultaneous facial landmark detection, head pose and
facial deformation estimation, even if the images are under facial occlusion.Comment: International Conference on Computer Vision and Pattern Recognition,
201
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