Face Recognition Based on Improved Subtraction Average-Based Optimizer and BP Neural Network
Chaos mapping was introduced in the initialization stage of subtraction average-based optimizer(SABO)and combined with golden sine algorithm.The improved subtraction average-based optimizer(ISABO)was proposed to solve the problem that the subtraction average-based optimizer might fall into the local optimal solution,and the effectiveness of ISABO was verified by the extremum optimization of 23 reference functions.Aiming at the problem of classification and recognition of static face images,this paper used histogram equalization processing method and Gaussian filter processing method successively for image preprocessing,and then used principal component analysis(PCA)to extract image features.Finally,the face recognition model ISABO-BP based on ISABO and BP neural network was established by optimizing BP neural network to realize face image classification.The experimental results show that the face recognition accuracy of the proposed ISABO-BP in ORL face database is 97.50%on average,which is better than other comparison algorithms,and has good stability,effectively reducing the error rate,rejection rate and error rejection ratio.
face recognitionprincipal component analysissubtraction average-based optimizergolden sine algorithmchaotic mappingBP neural network
万方数据
维普
