New soft biometrics for limited resource in keystroke dynamics authentication

Ting-Yi  Chang, Cheng Jung Tsai, Jen Yuan Yeh, Chun Cheng Peng, Pei Hsuan Chen

Research output: Contribution to journalArticlepeer-review


In recent years, some researches on free text authentication by keystroke dynamics have been proposed. The main problem of these proposed researches is the requirement of a very long training time. To increase users’ willingness to use the proposed authentication system, one simple and feasible way is shortening the training time. In this case, the training data is limited and is known as the limited resource problem. In this paper, we propose new soft biometrics and a new classifier for limited resources in free text authentication in English. Our new soft biometrics combines the idea of data mining and statistical prediction. Because our soft biometric is a mining result of limited resources, it is sensitive to outliers and a traditional statistical classifier cannot be applied. To solve this problem, the proposed classifiers considered the problem of an outlier and calculated the difference of cluster distribution. There are 114 participants in our experiments. Experimental results show that our approach can improve the accuracy of free text authentication in the case of limited resources.

Original languageEnglish
JournalMultimedia Tools and Applications
Publication statusAccepted/In press - 2020 Jan 1

All Science Journal Classification (ASJC) codes

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications

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