Baseline CNN structure analysis for facial expression recognition
This work provides a baseline for facial expression recognition, but it is incremental as it compares existing methods without introducing new techniques.
The authors tackled the problem of identifying the most efficient CNN structure and image preprocessing method for facial expression recognition, finding that a simple three-layer CNN with histogram equalization input achieved the best performance across multiple databases.
We present a baseline convolutional neural network (CNN) structure and image preprocessing methodology to improve facial expression recognition algorithm using CNN. To analyze the most efficient network structure, we investigated four network structures that are known to show good performance in facial expression recognition. Moreover, we also investigated the effect of input image preprocessing methods. Five types of data input (raw, histogram equalization, isotropic smoothing, diffusion-based normalization, difference of Gaussian) were tested, and the accuracy was compared. We trained 20 different CNN models (4 networks x 5 data input types) and verified the performance of each network with test images from five different databases. The experiment result showed that a three-layer structure consisting of a simple convolutional and a max pooling layer with histogram equalization image input was the most efficient. We describe the detailed training procedure and analyze the result of the test accuracy based on considerable observation.