Facial Expression Recognition using Visual Saliency and Deep Learning

Understand

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.