Abstract
During communication process, people’s emotions are usually shown on the face (we call it expression). Capturing expression signals is very important to communication among individuals. In this article, we develop a neuron network architecture for automatic recognition of seven facial expressions: angry, disgust, fear, happy, sad, surprise and neutral. Furthermore, we also developed an CycleGAN architecture used to learn the data allocation of the training data set to solve the problem of data imbalance of the problem need solving. The efficiency of the architectures was evaluated on the FER2013 data set with an accuracy of 72.24%, which is higher than the result of the winner in the FER2013 competition (71.16%).
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Nguyen, V.T. et al. (2021). Feature Learning and Data Generative Models for Facial Expression Recognition. In: Dang, T.K., Küng, J., Chung, T.M., Takizawa, M. (eds) Future Data and Security Engineering. FDSE 2021. Lecture Notes in Computer Science(), vol 13076. Springer, Cham. https://doi.org/10.1007/978-3-030-91387-8_10
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DOI: https://doi.org/10.1007/978-3-030-91387-8_10
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