  Automatic facial expression recognition is an important research area in the
emotion recognition and computer vision. Applications can be found in several
domains such as medical treatment, driver fatigue surveillance, sociable
robotics, and several other human-computer interaction systems. Therefore, it
is crucial that the machine should be able to recognize the emotional state of
the user with high accuracy. In recent years, deep neural networks have been
used with great success in recognizing emotions. In this paper, we present a
new model for continuous emotion recognition based on facial expression
recognition by using an unsupervised learning approach based on transfer
learning and autoencoders. The proposed approach also includes preprocessing
and post-processing techniques which contribute favorably to improving the
performance of predicting the concordance correlation coefficient for arousal
and valence dimensions. Experimental results for predicting spontaneous and
natural emotions on the RECOLA 2016 dataset have shown that the proposed
approach based on visual information can achieve CCCs of 0.516 and 0.264 for
valence and arousal, respectively.
