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The imbalanced number and the high similarity of samples in expression database can lead to overfitting in facial recognition neural networks. To address this problem, based on edge computing, a facial expression recognition method using deep convolutional neural networks is proposed. In order to overcome the shortcoming that circular consensus adversarial network model can only be mapped one-to-one, we construct a constrained circular consensus generative adversarial network by adding class constraint information. Discriminators and classifiers in this network can share network parameters. In addition, for the problems of unstable training and easy to encounter model collapse in original GAN networks, this paper introduces gradient penalty rule into discriminator's loss function to achieve the normative constraint on gradient changes. Using this network not only generates sample data for a few classes in the training set of expression database, but also performs effective expression classification. Compared with other methods, the improved discriminative classifier network structure can enhance the diversity of samples and get a higher expression recognition rate. Even if other expression feature extraction methods are used, the higher recognition rate can still be obtained after using proposed data augmentation framework.

A Facial Expression Recognition Method Using Deep Convolutional Neural Networks Based on Edge Computing