  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.
