  Deriving an effective facial expression recognition component is important
for a successful human-computer interaction system. Nonetheless, recognizing
facial expression remains a challenging task. This paper describes a novel
approach towards facial expression recognition task. The proposed method is
motivated by the success of Convolutional Neural Networks (CNN) on the face
recognition problem. Unlike other works, we focus on achieving good accuracy
while requiring only a small sample data for training. Scale Invariant Feature
Transform (SIFT) features are used to increase the performance on small data as
SIFT does not require extensive training data to generate useful features. In
this paper, both Dense SIFT and regular SIFT are studied and compared when
merged with CNN features. Moreover, an aggregator of the models is developed.
The proposed approach is tested on the FER-2013 and CK+ datasets. Results
demonstrate the superiority of CNN with Dense SIFT over conventional CNN and
CNN with SIFT. The accuracy even increased when all the models are aggregated
which generates state-of-art results on FER-2013 and CK+ datasets, where it
achieved 73.4% on FER-2013 and 99.1% on CK+.
