2,513 research outputs found
Regularizing Face Verification Nets For Pain Intensity Regression
Limited labeled data are available for the research of estimating facial
expression intensities. For instance, the ability to train deep networks for
automated pain assessment is limited by small datasets with labels of
patient-reported pain intensities. Fortunately, fine-tuning from a
data-extensive pre-trained domain, such as face verification, can alleviate
this problem. In this paper, we propose a network that fine-tunes a
state-of-the-art face verification network using a regularized regression loss
and additional data with expression labels. In this way, the expression
intensity regression task can benefit from the rich feature representations
trained on a huge amount of data for face verification. The proposed
regularized deep regressor is applied to estimate the pain expression intensity
and verified on the widely-used UNBC-McMaster Shoulder-Pain dataset, achieving
the state-of-the-art performance. A weighted evaluation metric is also proposed
to address the imbalance issue of different pain intensities.Comment: 5 pages, 3 figure; Camera-ready version to appear at IEEE ICIP 201
Interspecies Knowledge Transfer for Facial Keypoint Detection
We present a method for localizing facial keypoints on animals by
transferring knowledge gained from human faces. Instead of directly finetuning
a network trained to detect keypoints on human faces to animal faces (which is
sub-optimal since human and animal faces can look quite different), we propose
to first adapt the animal images to the pre-trained human detection network by
correcting for the differences in animal and human face shape. We first find
the nearest human neighbors for each animal image using an unsupervised shape
matching method. We use these matches to train a thin plate spline warping
network to warp each animal face to look more human-like. The warping network
is then jointly finetuned with a pre-trained human facial keypoint detection
network using an animal dataset. We demonstrate state-of-the-art results on
both horse and sheep facial keypoint detection, and significant improvement
over simple finetuning, especially when training data is scarce. Additionally,
we present a new dataset with 3717 images with horse face and facial keypoint
annotations.Comment: CVPR 2017 Camera Read
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