An Efficient CNN Model for Real-Time Facial Expression Recognition

In this paper, our group proposes and designs a lightweight convolutional neural network for recognizing facial expressions. Our network model reduces the number of parameters in the convolutional layer by eliminating the fully connected layer. And our model has no obviously adverse effect on detection and classification. Our model obtains the good detection results by identifying images outside the dataset, which proves that the model designed in this paper is suitable for multi classication of facial expressions. In general, we have realized a visual system that can be integrated on devices with low computing power to achieve facial expression classification and reduce a large number of parameters. After comparing with the models in recent years, the accuracy of our model is higher than theirs, and it has achieved good detection results in images outside the dataset from the experimental results.  

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