Open AccessA Face Recognition System Using ACO-BPNN Model for Optimizing the Teaching Management System
Author(s) -
Xiuli Zhu
Publication year - 2021
Publication title -
computational intelligence and neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.605
H-Index - 52
eISSN - 1687-5273
pISSN - 1687-5265
DOI - 10.1155/2021/5194044
Subject(s) - computer science , artificial intelligence , facial recognition system , pattern recognition (psychology) , feature extraction , face (sociological concept) , artificial neural network , feature (linguistics) , face detection , three dimensional face recognition , identification (biology) , password , computer vision , social science , linguistics , philosophy , botany , sociology , biology , computer network
The basic idea of face recognition technology is to compare the matching degree between the standard face image marked with identity information and the static or dynamic face collected from the actual scene, which includes two main research contents: face feature extraction and face feature recognition. Traditional identification generally proves who we are through certificates, passwords, or certificates plus passwords. With the development of science and technology, face recognition technology will occupy an increasingly important position. Inspired by the human brain, the artificial neural network (ANN) is an information extraction system based on imitating the basic function and structure of the human brain and abstracted by physical and mathematical research methods. Based on the traditional BP neural network model, this paper proposes an ant colony algorithm-enabled BP neural network (ACO-BPNN) model and applies it to face recognition. Experimental results show that, similar to other face recognition techniques, the facial feature location needs to adapt to various changes of faces to the maximum extent, so the recognition and classification effect of the whole face feature extracted from the whole face image on the changes of such partial areas is not good, while the local feature extraction method based on ACO-BPNN can achieve a good recognition and classification effect.