I Only Have Eyes for You: The Impact of Masks On Convolutional-Based Facial Expression Recognition

@article{Barros2021IOH,
  title={I Only Have Eyes for You: The Impact of Masks On Convolutional-Based Facial Expression Recognition},
  author={Pablo V. A. Barros and Alessandra Sciutti},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  year={2021},
  pages={1226-1231}
}
  • Pablo V. A. Barros, A. Sciutti
  • Published 16 April 2021
  • Computer Science
  • 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
The current COVID-19 pandemic has shown us that we are still facing unpredictable challenges in our society. The necessary constrain on social interactions affected heavily how we envision and prepare the future of social robots and artificial agents in general. Adapting current affective perception models towards constrained perception based on the hard separation between facial perception and affective understanding would help us to provide robust systems. In this paper, we perform an in… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 27 REFERENCES
The FaceChannel: A Fast and Furious Deep Neural Network for Facial Expression Recognition
TLDR
This paper formalizes the FaceChannel, a light-weight neural network that has much fewer parameters than common deep neural networks, and introduces an inhibitory layer that helps to shape the learning of facial features in the last layer of the network and improves performance while reducing the number of trainable parameters.
Mapping the emotional face. How individual face parts contribute to successful emotion recognition
TLDR
A similarity analysis of the usefulness of different face parts for expression recognition demonstrated that faces cluster according to the emotion they express, rather than by low-level physical features.
Deep Facial Expression Recognition: A Survey
TLDR
This survey provides a comprehensive review on deep FER, including datasets and algorithms that provide insights into overfitting caused by a lack of sufficient training data and expression-unrelated variations, such as illumination, head pose and identity bias.
Facial Emotion Recognition with Varying Poses and/or Partial Occlusion Using Multi-stage Progressive Transfer Learning
TLDR
Experimental results demonstrate that the proposed MSPTL approach outperforms typical TL and other PTL systems for FER in both frontal and non-frontal face poses.
Facial expression recognition with convolutional neural networks via a new face cropping and rotation strategy
TLDR
New face cropping and rotation strategies and simplification of the convolutional neural network (CNN) to make data more abundant and only useful facial features can be extracted and compete with existing methods in terms of training time, testing time, and recognition accuracy.
Wearing Face Masks Strongly Confuses Counterparts in Reading Emotions
  • C. Carbon
  • Psychology
    Frontiers in Psychology
  • 2020
TLDR
Compensatory actions that can keep social interaction effective (e.g., body language, gesture, and verbal communication), even when relevant visual information is crucially reduced are discussed.
Facial Expression Recognition Based on Deep Learning: A Survey
TLDR
The current research states are analyzed mostly from the latest facial expression extraction algorithm and the FER algorithm based on deep learning a comparison is made of these methods and the possible trends are outlined.
Deep Learning for Emotion Recognition on Small Datasets using Transfer Learning
This paper presents the techniques employed in our team's submissions to the 2015 Emotion Recognition in the Wild contest, for the sub-challenge of Static Facial Expression Recognition in the Wild.
An analysis of facial expression recognition under partial facial image occlusion
...
...