Title: Research on face recognition technology based on KPCA and SVM under convolutional filtering
Abstract: Face recognition is an important and difficult problem in the field of artificial intelligence and image vision. This paper studies the feature face principal component analysis (PCA) based on statistical features, which is used to reduce the gray dimension of face image extraction. Aiming at the problem that PCA can't handle the nonlinear reduction of face image information and the classification of prediction results well, the multi-classification voting algorithm of KPCA and SVM is adopted. Due to the limitations of the feature face technology, the feature extraction of the local sensitive area of the face is insufficient. The convolutional filter is designed to preprocess the image to enhance the extraction of the contour features of the face and the feature of the key area. Experimental results show that this method can improve the recognition accuracy.
Publication Year: 2021
Publication Date: 2021-06-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 1
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot