Christine L. Lisetti and David E. Rumelhart
We discuss the development of a neural network for facial expression recognition. It aims at recognizing and interpreting facial expressions in terms of signaled emotions and level of expressiveness. We use the backpropagation algorithm to train the system to differentiate between facial expressions. We show how the network generalizes to new faces and we analyze the results. In our approach, we acknowledge that facial expressions can be very subtle, and propose strategies to deal with the complexity of various levels of expressiveness. Our database includes a variety of different faces, including individuals of different gender, race, and including different features such as glasses, mustache, and beard. Even given the variety of the database, the network learns fairly succesfully to distinguish various levels of expressiveness, and generalizes on new faces as well.