38,174 research outputs found
Machine Analysis of Facial Expressions
No abstract
FML: Face Model Learning from Videos
Monocular image-based 3D reconstruction of faces is a long-standing problem
in computer vision. Since image data is a 2D projection of a 3D face, the
resulting depth ambiguity makes the problem ill-posed. Most existing methods
rely on data-driven priors that are built from limited 3D face scans. In
contrast, we propose multi-frame video-based self-supervised training of a deep
network that (i) learns a face identity model both in shape and appearance
while (ii) jointly learning to reconstruct 3D faces. Our face model is learned
using only corpora of in-the-wild video clips collected from the Internet. This
virtually endless source of training data enables learning of a highly general
3D face model. In order to achieve this, we propose a novel multi-frame
consistency loss that ensures consistent shape and appearance across multiple
frames of a subject's face, thus minimizing depth ambiguity. At test time we
can use an arbitrary number of frames, so that we can perform both monocular as
well as multi-frame reconstruction.Comment: CVPR 2019 (Oral). Video: https://www.youtube.com/watch?v=SG2BwxCw0lQ,
Project Page: https://gvv.mpi-inf.mpg.de/projects/FML19
Automatic 3D facial expression recognition using geometric and textured feature fusion
3D facial expression recognition has gained more and more interests from affective computing society due to issues such as pose variations and illumination changes caused by 2D imaging having been eliminated. There are many applications that can benefit from this research, such as medical applications involving the detection of pain and psychological effects in patients, in human-computer interaction tasks that intelligent systems use in today's world. In this paper, we look into 3D Facial Expression Recognition, by investigating many feature extraction methods used on the 2D textured images and 3D geometric data, fusing the 2 domains to increase the overall performance. A One Vs All Multi-class SVM Classifier has been adopted to recognize the expressions Angry, Disgust, Fear, Happy, Neutral, Sad and Surprise from the BU-3DFE and Bosphorus databases. The proposed approach displays an increase in performance when the features are fused together
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