Facial Expression Recognition using Visual Saliency and Deep Learning
This work addresses facial expression recognition for applications like human-computer interaction, but it is incremental as it builds on existing methods with minor modifications.
The paper tackled facial expression recognition by fine-tuning a pre-trained CNN on two datasets, achieving test accuracies of 74.79% and 95.71%, and used visual saliency maps to improve generalization, resulting in a top-1 accuracy of 65.39% in cross-dataset tests.
We have developed a convolutional neural network for the purpose of recognizing facial expressions in human beings. We have fine-tuned the existing convolutional neural network model trained on the visual recognition dataset used in the ILSVRC2012 to two widely used facial expression datasets - CFEE and RaFD, which when trained and tested independently yielded test accuracies of 74.79% and 95.71%, respectively. Generalization of results was evident by training on one dataset and testing on the other. Further, the image product of the cropped faces and their visual saliency maps were computed using Deep Multi-Layer Network for saliency prediction and were fed to the facial expression recognition CNN. In the most generalized experiment, we observed the top-1 accuracy in the test set to be 65.39%. General confusion trends between different facial expressions as exhibited by humans were also observed.