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Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment
Facial action unit (AU) detection and face alignment are two highly
correlated tasks since facial landmarks can provide precise AU locations to
facilitate the extraction of meaningful local features for AU detection. Most
existing AU detection works often treat face alignment as a preprocessing and
handle the two tasks independently. In this paper, we propose a novel
end-to-end deep learning framework for joint AU detection and face alignment,
which has not been explored before. In particular, multi-scale shared features
are learned firstly, and high-level features of face alignment are fed into AU
detection. Moreover, to extract precise local features, we propose an adaptive
attention learning module to refine the attention map of each AU adaptively.
Finally, the assembled local features are integrated with face alignment
features and global features for AU detection. Experiments on BP4D and DISFA
benchmarks demonstrate that our framework significantly outperforms the
state-of-the-art methods for AU detection.Comment: This paper has been accepted by ECCV 201
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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