Abstract
The present study focuses on using a new model to perform view-independent hum an face recognition. A model based on mixture of experts is proposed, which uses teacher-directed learning method to force the experts to learn a predetermined partitioning of the input face space, using a Radial basis function neural network for Gating network. This way, each expert obtains expertise over faces of a same pose. Experimental results on the PIE dataset demonstrated the improved performance of our proposed model in comparison with ME in its conventional learning style in terms of higher recognition rate.
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Khaleghian, S., Makhsoos, N.T., Ebrahimpour, R., Hajiany, A. (2010). View-Independent Face Recognition with RBF Gating in Mixture of Experts Method by Teacher-Directed Learning. In: Iskander, M., Kapila, V., Karim, M. (eds) Technological Developments in Education and Automation. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3656-8_75
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DOI: https://doi.org/10.1007/978-90-481-3656-8_75
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