Application of Face Recognition in E-commerce Security Authentication in the Era of Big Data. (12th October 2022)

• Record Type: Journal Article
• Authors: Chang, Shu; Duan, Youtao
• Other Names: Venkatesan C. Academic Editor.
• Is Part Of: Security and communication networks. Volume 2022(2022)
• Publication Date: 2022-10-12
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2022/4246750
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : Although Internet technology brings invaluable benefits to all walks of life, the security of network and information is becoming more and more prominent. The leakage of personnel information caused by Internet security incidents causes irreparable harm and loss to individuals or enterprises. E-commerce is a virtual transaction mode based on Internet technology. Its security requirements for transactions are more stringent than those for traditional transaction modes. Traditional identity authentication technology can no longer meet its security needs. People urgently need a reliable identity authentication system, meaning to ensure the security of e-commerce. According to the actual application scenarios of the algorithm in e-commerce, this paper, based on the research on face recognition technology, focuses on the in-depth research on face detection technology under the background of big data in order to introduce face recognition technology in this paper. This paper proposes a feature-based face matching algorithm. The face image is preprocessed to improve the accuracy of real-time face detection and reduce the false detection rate. Based on the research of traditional facial feature extraction technology, a virtual sample set that can effectively support a traditional facial feature extraction algorithm is constructed to solve the problem of insufficient training samples in practical applications. The experimental results showed that the accuracy of the method in this paper can reach up to 79.5% and that the minimum time consumption is only 0.142 s. Compared with the traditional method, the accuracy rate is higher and the time consumption is shorter.