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《Computer Applications and Software》 2019-02
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Wang Haiyan;College of Intelligent Manufacturing,Sichuan University of Arts and Science;  
Face detection and recognition is affected by external conditions has low recognition rate.To solve this problem,we proposed a face recognition method based on binary image Logistic regression and back-propagation neural network(BPNN).The algorithm converted the color images to grayscale images.A low-pass filter was used to remove the noise.The local window standard deviation and adaptive threshold were applied to grayscale images so as to obtain high-quality binary denoised images from which possible face regions were detected.We adopted the nearest neighbor interpolation method to reduce them.A face database was created corresponding to each reduced-size image.We used Logistic regression and BPNN to classify all images belonging to each person,and obtained a decision boundary for each type of image.The reduction in image size minimized the computational space and time of logistic regression and neural network training.Experimental results show that the accuracy of Logistic regression and BPNN is as high as 97.5% in the FEI image database,which is better than the accuracy of other recognition algorithms.
【Fund】: 四川省教育厅科研项目(16ZB0360)
【CateGory Index】: TP391.41;TP183
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