Improved LDA and LVQ for Face Recognition
Aijia Ouyang, Kenli Li, Xu Zhou, Yuming Xu, Guangxue Yue, Tan Lizhi · Applied Mathematics & Information Sciences · 2013
We present a hybrid face recognition algorithm which is based on the linear discriminant analysis (LDA) improved by a fusion technique and learning vector quantization (LVQ) in the paper. Firstly, the improved LDA is utilized to reduce the sample vector dimension, and then the LVQ classifier is used to recognize human faces. We perform intensive set of simulation experiments and results show that the algorithm not only reduces the computation time of the entire algorithm, but also improves the algorithm classification. More accurately, the algorithm obtains higher classification capability for the standard databases such as ORL, Yale and AR.