Abstract
With the advent of the era of artificial intelligence and big data, intelligent security robots not only improve the efficiency of the traditional intelligent security industry but also propose higher requirements for intelligent security. Aiming to solve the problem of long recognition time and high equipment cost of intelligent security robots, we propose a new face recognition method for intelligent security in this paper. We use the Goldstein branching method for phase unwrapping, which can improve the three-dimensional (3D) face reconstruction effect. Subsequently, by using the three-dimensional face recognition method based on face radial curve elastic matching, different weights are assigned to different curve recognition similarity for weighted fusion as the total similarity for recognition. Experiments show that the method has a higher face recognition rate and is robust to attitude, illumination, and noise.
Highlights
IntroductionThe domestic security industry market has developed rapidly
In recent years, the domestic security industry market has developed rapidly
In order to improve the efficiency of face reconstruction and face recognition of intelligent security robots, we propose a new method for face recognition of intelligent security in this paper
Summary
The domestic security industry market has developed rapidly. In 2019, the market size of China’s security industry was approximately. It is estimated that in 2020, intelligent security will create a market worth about. 100 billion yuan, and intelligent security will be an important market in the security field. There is a wide range of applications of the Internet of Things in smart cities, civil security, and some focusing industries. Face recognition technology research based on big data systems is of great significance. With the technology of big data and artificial intelligence developing rapidly, the big data environment provides a good basis for the in-depth development of face recognition systems and realizes the sharing of feature databases in wider fields, which is helpful for achieving more abundant face feature databases
Sign-in/Register to view highlights & summary 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 callDisclaimer: 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.
