  We propose a convolutional neural network (CNN) architecture for facial
expression recognition. The proposed architecture is independent of any
hand-crafted feature extraction and performs better than the earlier proposed
convolutional neural network based approaches. We visualize the automatically
extracted features which have been learned by the network in order to provide a
better understanding. The standard datasets, i.e. Extended Cohn-Kanade (CKP)
and MMI Facial Expression Databse are used for the quantitative evaluation. On
the CKP set the current state of the art approach, using CNNs, achieves an
accuracy of 99.2%. For the MMI dataset, currently the best accuracy for emotion
recognition is 93.33%. The proposed architecture achieves 99.6% for CKP and
98.63% for MMI, therefore performing better than the state of the art using
CNNs. Automatic facial expression recognition has a broad spectrum of
applications such as human-computer interaction and safety systems. This is due
to the fact that non-verbal cues are important forms of communication and play
a pivotal role in interpersonal communication. The performance of the proposed
architecture endorses the efficacy and reliable usage of the proposed work for
real world applications.
