In the process of facial expression recognition by convolutional neural network, aiming at the problem that the complex background interferes with the extraction of expression features, a simple face cropping strategy is proposed. First, the critical facial expression regions are calculated by face alignment and landmarks detection, thus the background influence outside the facial expression region is reduced, and then the convolutional neural network is used to further extract expression features and enable expression classification. The experimental results show that the facial expression recognition effect is significantly improved by the proposed method, and the recognition accuracy on the facial expression datasets JAFFE and CK+ reaches 90.48% and 96.67%, respectively.
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