Behavioral Biometrics Based on Human-Computer Interaction Devices

  • Chi LinEmail author
  • Mohammad S. Obaidat


The purpose of this chapter is to describe a new approach to recognize the identity of a person through analyzing the behavioral biometrics in Wi-Fi signals and their potential application prospects. A solid understanding of processing Wi-Fi signals helps to interpret solid information and problem statement on identity recognition through Wi-Fi signals. The ubiquitous and temporal features of Wi-Fi signals are the basis of recognition and localization. We introduce a new paradigm on how to use Wi-Fi signals to identify the human in the open environment. We proposed Wide, a Wi-Fi signal-based human identity recognition system. First, we describe the components of Wide and how it works in detail. Through collecting CSI (channel state information) profiles, Wide is able to recognize the human identity through sampling and extracting features of the received Wi-Fi signals. Then, to reduce the storage overhead while guaranteeing high recognizing accuracy, principal component analysis (PCA) technique is used. Finally, test-bed experiments are conducted to show the performance of Wide, indicating that Wide can quickly recognize people in a high accuracy.

The chapter starts with the definition of Wi-Fi signals and CSI (channel state information) and behavioral biometrics-related applications. Particular emphasis is placed on the characteristic of the CSI, which indicates that CSI can be used for recognizing the identity of people. Then we highlight our objective and demonstrate our design in detail. At last, experiments are conducted through collecting, analyzing, and processing Wi-Fi signals to recognize the identity of people, revealing that the proposed scheme can recognize people with promising accuracy in a short time.

This chapter is structured as follows. Section 1 focuses on research background in behavioral biometrics and illustrations on characteristics of key technologies. Section 2 gives a brief overview on related achievement in this research field. Section 3 looks at the essence of related theory and behavioral biometric recognition methods. Section 4 deals with experimental installations and configurations. Section 5 analyzes the experimental results and discusses the potential features of our scheme. Section 6 concludes this chapter and outlines future research trends in Wi-Fi signal topics.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Software, Dalian University of TechnologyDalianChina
  2. 2.Key Laboratory for Ubiquitous Network and Service Software of Liaoning ProvinceDalianChina
  3. 3.Department of Computer and Information ScienceFordham UniversityBronxUSA

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