2 research outputs found
Deep Multi-Center Learning for Face Alignment
Facial landmarks are highly correlated with each other since a certain
landmark can be estimated by its neighboring landmarks. Most of the existing
deep learning methods only use one fully-connected layer called shape
prediction layer to estimate the locations of facial landmarks. In this paper,
we propose a novel deep learning framework named Multi-Center Learning with
multiple shape prediction layers for face alignment. In particular, each shape
prediction layer emphasizes on the detection of a certain cluster of
semantically relevant landmarks respectively. Challenging landmarks are focused
firstly, and each cluster of landmarks is further optimized respectively.
Moreover, to reduce the model complexity, we propose a model assembling method
to integrate multiple shape prediction layers into one shape prediction layer.
Extensive experiments demonstrate that our method is effective for handling
complex occlusions and appearance variations with real-time performance. The
code for our method is available at
https://github.com/ZhiwenShao/MCNet-Extension.Comment: This paper has been accepted by Neurocomputin
JA-Net: Joint Facial Action Unit Detection and Face Alignment via Adaptive Attention
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.
However, most existing AU detection works handle the two tasks independently by
treating face alignment as a preprocessing, and often use landmarks to
predefine a fixed region or attention for each AU. 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 feature is learned firstly, and high-level feature of face alignment is
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 feature and global feature for AU detection. Extensive experiments
demonstrate that our framework (i) significantly outperforms the
state-of-the-art AU detection methods on the challenging BP4D, DISFA, GFT and
BP4D+ benchmarks, (ii) can adaptively capture the irregular region of each AU,
(iii) achieves competitive performance for face alignment, and (iv) also works
well under partial occlusions and non-frontal poses. The code for our method is
available at https://github.com/ZhiwenShao/PyTorch-JAANet.Comment: This paper is the extended version of arXiv:1803.05588, and is
accepted by International Journal of Computer Visio