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EmotioNet
Classification
Face
|...
License: Unknown

Overview

The EmotioNet database includes 950,000 images with annotated AUs. These were annotated with
the algorithm described in

Benitez-Quiroz, C. F., Srinivasan, R., & Martinez, A. M. (2016).
EmotioNet: An accurate, real-time algorithm for the automatic annotation of a million facial
expressions in the wild. In Proceedings of IEEE International Conference on Computer Vision
& Pattern Recognition (CVPR'16), Las Vegas, NV, USA.

You can train your system using this
set. You can also use any other annotated dataset you think appropriate. This dataset has been
used to successfully train a variety of classifiers, including several deep networks.We also
include 25K (24,600 to be precise) manually annotated AUs. You may want to use this dataset
to see how well your algorithm works or to optimize the parameters of your algorithm.

Data Summary
Type
Image,
Amount
975K
Size
--
Provided by
The Ohio State University Computational Biology and Cognitive Science Lab
Our lab focuses on the theoretical aspects of cognitive science and neuroscience, with a particular emphasis on vision, learning and linguistics. We combine computational modeling with imaging and behavioral data to acquire a broader understanding of how the human brain functions. This includes research in machine learning and computer vision.
| Amount 975K | Size --
EmotioNet
Classification
Face
License: Unknown

Overview

The EmotioNet database includes 950,000 images with annotated AUs. These were annotated with
the algorithm described in

Benitez-Quiroz, C. F., Srinivasan, R., & Martinez, A. M. (2016).
EmotioNet: An accurate, real-time algorithm for the automatic annotation of a million facial
expressions in the wild. In Proceedings of IEEE International Conference on Computer Vision
& Pattern Recognition (CVPR'16), Las Vegas, NV, USA.

You can train your system using this
set. You can also use any other annotated dataset you think appropriate. This dataset has been
used to successfully train a variety of classifiers, including several deep networks.We also
include 25K (24,600 to be precise) manually annotated AUs. You may want to use this dataset
to see how well your algorithm works or to optimize the parameters of your algorithm.

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