Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Handbook Of Pattern Recognition And Computer Vision
Handbook Of Pattern Recognition And Computer Vision
Which model to use for the liquid state machine?
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Hi-index | 0.00 |
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.