Action unit selective feature maps in deep networks for facial expression recognition

@article{Zhou2017ActionUS,
  title={Action unit selective feature maps in deep networks for facial expression recognition},
  author={Yuqian Zhou and B. Shi},
  journal={2017 International Joint Conference on Neural Networks (IJCNN)},
  year={2017},
  pages={2031-2038}
}
  • Yuqian Zhou, B. Shi
  • Published 2017
  • Computer Science
  • 2017 International Joint Conference on Neural Networks (IJCNN)
Facial expression recognizers based on handcrafted features have achieved satisfactory performance on many databases. Recently, deep neural networks, e. g. deep convolutional neural networks (CNNs) have been shown to boost performance on vision tasks. However, the mechanisms exploited by CNNs are not well established. In this paper, we establish the existence and utility of feature maps selective to action units in a deep CNN trained by transfer learning. We transfer a network pre-trained on… Expand
Adaptive Weighting of Handcrafted Feature Losses for Facial Expression Recognition
Pose-Independent Facial Action Unit Intensity Regression Based on Multi-Task Deep Transfer Learning
  • Yuqian Zhou, Jimin Pi, B. Shi
  • Computer Science
  • 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)
  • 2017
The FaceChannel: A Fast and Furious Deep Neural Network for Facial Expression Recognition
The FaceChannel: A Fast & Furious Deep Neural Network for Facial Expression Recognition
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