Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IOP Conference Series: Materials Science and Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
