Abstract: Aiming at the situation of large calculation amount and slow calculation speed of existing kernel subspace face recognition algorithm,this paper presented a fast kernel subspace face recognition algorithm based on neural network model,and used neuron of neural network hidden layer to reduce base representation of nuclear feature subspace in order to improve the speed of recognition.Then we established the neural network approximation model based on the KPCA and KFDA kernel subspace face recognition algorithms and made an analysis on the database of ORL,UMIST and YALE.The experimental results show that when the number of the hidden layer neuron is set as half of the training samples or less,the fast kernel subspace algorithm based on neural network can achieve similar or even equal recognition rate to the nuclear subspace algorithm,and the recognition time can be reduced to 50% or even lower with certain correct recognition rate.

Key words: Face recognition,Feature extraction,KPCA,KFDA,Neural network