Artificial Intelligence-Based Behavioral Biometrics

View Sample PDF

Author(s): Muskan Gupta (Vellore Institute of Technology, Vellore, India)and B. K. Tripathy (Vellore Institute of Technology, Vellore, India)
Copyright: 2023
Pages: 12
Source title: Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch051

Keywords: Data Analysis and Statistics / Information Science Reference / Knowledge Management and Library Science / Machine Learning

Purchase

View Artificial Intelligence-Based Behavioral Biometrics on the publisher's website for pricing and purchasing information.

Abstract

Recognition of reliability of people entering into a system has been a vital downside in numerous business concerns. Biometrics, which provides recognition of personnel using their distinctive traits, has the potential to become an authentic approach and hence are irreplaceable as a part of several identification systems. Uni-modal biometric systems have been designed to validate user identity. These systems use single biometric traits that can be used for recognition. But most of the drawbacks in uni-modal biometric system are due to the use of single attribute of biometric. As a solution to this problem, multimodal biometric identification systems have been developed recently, which are useful for authentication of the users. These systems avoid security threats better and provide higher security to the systems. In this article, the authors present these aspects of behavioral biometrics based on artificial intelligence and provide a comparative study of the existing approaches and systems. Also, some suggestions for future enhancements are presented for better security of the systems.

The IRMA Community

Research IRM

Artificial Intelligence-Based Behavioral Biometrics

Purchase

Abstract

Related Content

IRMA Sponsors