Face recognition algorithm based on LLE and Fisher linear discrimination
Qiang Fan · Modern Electronics Technique · 2012
A method that combines the nonlinear down-dimentioned method with Fisher linear discrimination is presented to improve the recognition rates of face recognition algorithm based on popular learning theory.Firstly,the dimensions of face image tested and training set data are reduced to an appropriate dimensionality through threshold embedding algorithm,and then the Fisher linear discrimination is used to extract the face features.Finaly,the features of testing and training face images are classified by nearest neighbor classifier.The comparison between the face recognition algorithm based on LLE and Fisher linear discrimination and the typical face recognition algorithm based on the popular learning theory was conducted in Olivettifaces and ORL face image databases.The results show that the algorithm proposed in this paper has the highest recognition rate compared with some classical methods when the number of the nearest neighbor is large.