Open AccessFace Recognition Method Based on Probabilistic Neural Network Optimizing Two-Dimensional Subspace Analysis
Author(s) -
Haiyan Zhang,
Fenqi Qiao
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/719/1/012074
Subject(s) - pattern recognition (psychology) , artificial intelligence , subspace topology , computer science , facial recognition system , linear discriminant analysis , probabilistic neural network , face (sociological concept) , probabilistic logic , noise (video) , artificial neural network , feature extraction , identification (biology) , wavelet , image (mathematics) , time delay neural network , social science , botany , sociology , biology
If there is noise in the original image of face recognition, the efficiency of face recognition will be affected. In this paper, a face recognition method based on probabilistic neural network optimizing two-dimensional subspace analysis was proposed. Firstly, discrete wavelet variation was used to preprocess the image, and then two-dimensional linear discriminant analysis was used for feature extraction. Finally, the probabilistic neural network was used to complete the face classification. According to the results of experiments conducted on ORL and Fei general face database and the database collected independently, the recognition rate can also be as high as 98.9% when noise is added, and compared with several new identification methods, this method can achieve better identification performance.