  Facial expression recognition has been an active area in computer vision with
application areas including animation, social robots, personalized banking,
etc. In this study, we explore the problem of image classification for
detecting facial expressions based on features extracted from pre-trained
convolutional neural networks trained on ImageNet database. Features are
extracted and transferred to a Linear Support Vector Machine for
classification. All experiments are performed on two publicly available
datasets such as JAFFE and CK+ database. The results show that representations
learned from pre-trained networks for a task such as object recognition can be
transferred, and used for facial expression recognition. Furthermore, for a
small dataset, using features from earlier layers of the VGG19 network provides
better classification accuracy. Accuracies of 92.26% and 92.86% were achieved
for the CK+ and JAFFE datasets respectively.
