The partially observable hidden Markov model and its application to keystroke dynamics
作者：
Highlights：
• The partially observable hidden Markov model (POHMM) is introduced.
• In keystroke dynamics, the key names partially reveal typist behavior.
• The POHMM hidden state is conditioned on an independent Markov chain.
• The marginalized POHMM is equivalent to the HMM.
• A method of POHMM parameter smoothing is described.
• We perform user identification, verification, and continuous verification.
摘要
•The partially observable hidden Markov model (POHMM) is introduced.•In keystroke dynamics, the key names partially reveal typist behavior.•The POHMM hidden state is conditioned on an independent Markov chain.•The marginalized POHMM is equivalent to the HMM.•A method of POHMM parameter smoothing is described.•We perform user identification, verification, and continuous verification.
论文关键词：Hidden Markov model,Keystroke biometrics,Behavioral biometrics,Time intervals,Anomaly detection
论文评审过程：Received 2 December 2016, Revised 3 November 2017, Accepted 16 November 2017, Available online 21 November 2017, Version of Record 21 December 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.11.021
