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- W2973175029 abstract "Facial expression recognition is vital to many intelligent applications such as human-computer interaction and social networks. For machines, learning to classify six basic human expressions (anger, disgust, fear, happiness, sadness and surprise) is still a big challenge. This paper proposed a convolutional neural network based on AlexNet combining a Bayesian network. Besides traditional features, the relationships between facial action units (AU) and expressions are captured. Firstly, a convolutional neural network to extract features from images is constructed. Then, a Bayesian network is established to learn the dependencies of AUs and expressions from joint probabilities and conditional probabilities. Finally, ensemble learning is used to combine the features of expressions, AUs and dependencies between the two. Our experiments on popular datasets show that the proposed method performs well compared with latest approaches." @default.
- W2973175029 created "2019-09-19" @default.
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- W2973175029 date "2019-01-01" @default.
- W2973175029 modified "2023-10-02" @default.
- W2973175029 title "Action Unit Assisted Facial Expression Recognition" @default.
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- W2973175029 doi "https://doi.org/10.1007/978-3-030-30508-6_31" @default.
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