Deep learning for real-time robust facial expression recognition on a smartphone

We developed a real-time robust facial expression recognition function on a smartphone. To this end, we trained a deep convolutional neural network on a GPU to classify facial expressions. The network has 65k neurons and consists of 5 layers. The network of this size exhibits substantial overfitting when the size of training examples is not large. To combat overfitting, we applied data augmentation and a recently introduced technique called "dropout". Through experimental evaluation over various face datasets, we show that the trained network outperformed a classifier based on hand-engineered features by a large margin. With the trained network, we developed a smartphone app that recognized the user's facial expression. In this paper, we share our experiences on training such a deep network and developing a smartphone app based on the trained network.