Face Recognition Method Based on Probabilistic Neural Network Optimizing Two-Dimensional Subspace Analysis

摘要

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.