Affiliations: [a]
Key Lab. of Machine Learning and Computational Intelligence, College of Mathematics and Information Science, Hebei University, Baoding, China
| [b]
College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua, China
| [c]
Hebei Branch of Meteorological Cadres Training Institute, China Meteorological Administration, Baoding, China
Abstract: Probabilistic neural network (PNN) is simple and can be easily implemented. PNN has fast learning speed, and its outputs are posterior probabilities which facilitate the combination of classifiers with fuzzy integral. In this paper, we proposed a face recognition algorithm named EPNN, which combine PNN classifiers with fuzzy integral, and can make full use of the superiority of PNN and ensemble learning. The proposed method includes three stages: (1) the incomplete wavelet packet decomposition of face images; (2) training PNN classifiers with wavelet sub-images with low frequency components. (3) combination of the trained PNN classifiers by fuzzy integral. Compared with four matrix subspace algorithms, the proposed method can obtain competitive performance. Such as, it can improve the accuracy of face recognition with less CPU time. The experimental results on JAFFE, YALE, ORL and FERET confirm that the proposed method outperform the four matrix subspace algorithms.
