80,573 research outputs found
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
Facial Point Detection using Boosted Regression and Graph Models
Finding fiducial facial points in any frame of a video showing rich naturalistic facial behaviour is an unsolved problem. Yet this is a crucial step for geometric-featurebased facial expression analysis, and methods that use appearance-based features extracted at fiducial facial point locations. In this paper we present a method based on a combination of Support Vector Regression and Markov Random Fields to drastically reduce the time needed to search for a pointās location and increase the accuracy and robustness of the algorithm. Using Markov Random Fields allows us to constrain the search space by exploiting the constellations that facial points can form. The regressors on the other hand learn a mapping between the appearance of the area surrounding a point and the positions of these points, which makes detection of the points very fast and can make the algorithm robust to variations of appearance due to facial expression and moderate changes in head pose. The proposed point detection algorithm was tested on 1855 images, the results of which showed we outperform current state of the art point detectors
- ā¦