Deep learning for facial expression recognition: A step closer to a smartphone that knows your moods
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Bazrafkan, Shabab, Nedelcu, Tudor, Filipczuk, Pawel, & Corcoran, Peter. (2017). Deep learning for facial expression recognition: A step closer to a smartphone that knows your moods. Paper presented at the 2017 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 08-10 January.
By growing the capacity and processing power of the handheld devices nowadays, a wide range of capabilities can be implemented in these devices to make them more intelligent and user friendly. Determining the mood of the user can be used in order to provide suitable reactions from the device in different conditions. One of the most studied ways of mood detection is by using facial expressions, which is still one of the challenging fields in pattern recognition and machine learning science.Deep Neural Networks (DNN) have been widely used in order to overcome the difficulties in facial expression classification. In this paper it is shown that the classification accuracy is significantly lower when the network is trained with one database and tested with a different database. A solution for obtaining a general and robust network is given as well.