A Lightweight Convolutional Neural Network for Real-Time Facial Expression Detection

In this paper our group proposes and designs a lightweight convolutional neural network (CNN) for detecting facial emotions in real-time and in bulk to achieve a better classification effect. We verify whether our model is effective by creating a real-time vision system. This system employs multi-task cascaded convolutional networks (MTCNN) to complete face detection and transmit the obtained face coordinates to the facial emotions classification model we designed firstly. Then it accomplishes the task of emotion classification. Multi-task cascaded convolutional networks have a cascade detection feature, one of which can be used alone, thereby reducing the occupation of memory resources. Our expression classification model employs Global Average Pooling to replace the fully connected layer in the traditional deep convolution neural network model. Each channel of the feature map is associated with the corresponding category, eliminating the black box characteristics of the fully connected layer to a certain extent. At the same time, our model marries the residual modules and depth-wise separable convolutions, reducing large quantities of parameters and making the model more portable. Finally, our model is tested on the FER-2013 dataset. It only takes 3.1% of the 16GB memory, that is, only 0.496GB memory is needed to complete the task of classifying facial expressions. Not only can our model be stored in an 872.9 kilobytes file, but also its accuracy has reached 67% on the FER-2013 dataset. And it has good detection and recognition effects on those figures which are out of the dataset.