Facial expression recognition based on Liquid State Machines built of alternative neuron models

This paper presents an approach to facial expression recognition based on the theory of liquid computing. Up to date, no emotion recognition systems based on spiking neural networks exist, and our work is the first attempt in this direction. We investigated the pattern recognition ability of Liquid State Machines based on various neural models, such as integrate-and-fire, resonate-and-fire, FitzHugh-Nagumo, Morris-Lecal, Hindmarsh-Rose and Izhikevich's models. No single Liquid State Machine provided particularly good results, but a global classifier we defined merging the response of the different models achieved a very satisfactory performance in expression recognition.