Open AccessFace Detection and Recognition Method Based on Improved Convolutional Neural Network
Author(s) -
Zhengqiu Lu,
Chunliang Zhou,
Xuyang Xuyang,
Weipeng Zhang
Publication year - 2021
Publication title -
international journal of circuits, systems and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.156
H-Index - 13
ISSN - 1998-4464
DOI - 10.46300/9106.2021.15.85
Subject(s) - convolutional neural network , pooling , artificial intelligence , computer science , pattern recognition (psychology) , deep learning , face (sociological concept) , facial recognition system , feature (linguistics) , artificial neural network , neocognitron , time delay neural network , social science , linguistics , philosophy , sociology
with rapid development of deep learning technology, face recognition based on deep convolutional neural network becomes one of the main research methods. In order to solve the problems of information loss and equal treatment of each element in the input feature graph in the traditional pooling method of convolutional neural network, a face recognition algorithm based on convolutional neural network is proposed in this paper. First, MTCNN algorithm is used to detect the faces and do gray processing, and then a local weighted average pooling method based on local concern strategy is designed and a convolutional neural network based on VGG16 to recognize faces is constructed which is finally compared with common convolutional neural network. The experimental results show that this method has good face recognition accuracy in common face databases.