Face recognition using Eigenfaces-Fisher Linear Discriminant and Dynamic Fuzzy Neural Network
Tangquan Qi, Huiwen Deng, Weiping Hu · 2010
In order to solve the problem of face recognition in natural illumination, a new face recognition algorithm using Eigenface-Fisher Linear Discriminant (EFLD) and Dynamic Fuzzy Neural Network (DFNN) is proposed in this paper, which can solve the dimension of feature, and deal with the problem of classification easily. In this paper, we use EFLD model to extract the face feature, which will be considered as the input of the DFNN. And the DFNN is implemented as a classifier to solve the problem of classification. The proposed algorithm has been tested on ORL face database. The experiment results show that the algorithm reduces the dimension of face feature and finds a best subspace for the classification of human face. And by optimizing the architecture of dynamic fuzzy neural network reduces the classification error and raises the correct recognition rate. So the algorithm works well on face database with different expression, pose and illumination.