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Password based applications are commonly used in our daily lives such as the social networks, e-mails, e-commerce, and e-banking. Given the increasing number of hacker attacks, the only use of passwords is not enough to protect personal data and does not meet usability requirements. Keystroke dynamics is a promising solution that decreases the vulnerability of passwords to guessing attacks by analyzing the typing manner of the user. Despite its efficiency in the discrimination between users, it remains non-industrialized essentially due to the tedious learning phase and the intra-class variation of the users' characteristics. In this paper, we propose a double serial mechanism to adapt the user's model over time. An important property of the proposed solution relies in its usability as we only use a single sample as user's reference during the account creation. We demonstrate that the proposed method offers competitive performances while keeping a high usability.