Deep learning based FACS Action Unit occurrence and intensity estimation

Amogh Gudi · Tim M. den Uyl · Andreas Maroulis

178 citations0/0 files storedInternational Conference on Automatic Face and Gesture Recognition

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Emotion and Mood Recognition Face recognition and analysis Face and Expression Recognition

Abstract

Ground truth annotation of the occurrence and intensity of FACS Action Unit (AU) activation requires great amount of attention. The efforts towards achieving a common platform for AU evaluation have been addressed in the FG 2015 Facial Expression Recognition and Analysis challenge (FERA 2015). Participants are invited to estimate AU occurrence and intensity on a common benchmark dataset. Conventional approaches towards achieving automated methods are to train multiclass classifiers or to use regression models. In this paper, we propose a novel application of a deep convolutional neural network (CNN) to recognize AUs as part of FERA 2015 challenge. The 7 layer network is composed of 3 convolutional layers and a max-pooling layer. The final fully connected layers provide the classification output. For the selected tasks of the challenge, we have trained two different networks for the two different datasets, where one focuses on the AU occurrences and the other on both occurrences and intensities of the AUs. The occurrence and intensity of AU activation are estimated using specific neuron activations of the output layer. This way, we are able to create a single network architecture that could simultaneously be trained to produce binary and continuous classification output.

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References

1980Facial signs of emotional experience.2003Toward an affect-sensitive multimodal human-computer interaction 2011Emotion-Induced Engagement in Internet Video Advertisements 2011The first facial expression recognition and analysis challenge 2015FERA 2015 - second Facial Expression Recognition and Analysis challenge

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2017Enhancing CNN with Preprocessing Stage in Automatic Emotion Recognition 2019Emotion recognition from facial expression using deep convolutional neural network 2023Identifying emotions from facial expressions using a deep convolutional neural network-based approach 2021Facial emotion recognition using convolutional neural networks 2017Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition 2017Audio-visual emotion recognition using deep transfer learning and multiple temporal models 2017A Deep Learning Perspective on the Origin of Facial Expressions 2020Facial Expression Recognition With Deeply-Supervised Attention Network

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