Abstract
Expression recognition is an essential subfield in biometric recognition as well as a cutting-edge research area in computer vision. It has several uses in autonomous driving, human-computer interface, autism research, and other fields. This study offers an expression recognition model based on fusion attention that extracts expression features from the channel and spatial domains, respectively, then combines the two features. It is then trained on the FER2013 and ExpW datasets. Experiment findings suggest that the model may greatly enhance face emotion recognition accuracy.
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