Biometric is an emerging technique for user authentication thanks to its efficiency compared to the traditional methods, such as passwords and access-cards. However, most existing biometric authentication systems require the cooperation of users and provide only a login time authentication. To address these drawbacks, we propose in this paper a new, efficient continuous authentication scheme based on the newly biometric trait that still under development: prehensile movements. In this work, we model the movements through Hidden Markov Model-Universal Background Model (HMM-UBM) with continuous observations based on Gaussian Mixture Model (GMM). Unlike the literature, the gravity signal is included. The results of the experiments conducted on a public database HMOG and on a proprietary database, collected under uncontrolled conditions, have shown that prehensile movements are very promising. This new biometric feature will allow users to be authenticated continuously, passively and in real time.

与传统方法（例如密码和访问卡）相比，生物识别技术具有很高的效率，因此它是一种用于用户身份验证的新兴技术。但是，大多数现有的生物特征认证系统需要用户的配合，并且仅提供登录时间认证。为了解决这些缺点，我们在本文中提出了一种新的，有效的连续认证方案，该方案基于仍在发展中的新生物特征：羽毛运动。在这项工作中，我们通过隐马尔可夫模型-通用背景模型（HMM-UBM）对运动进行建模，并基于高斯混合模型（GMM）进行连续观察。与文献不同，包括重力信号。在不受控制的条件下收集的在公共数据库HMOG和专有数据库上进行的实验结果，已经表明，有力的运动很有前途。这项新的生物特征功能将使用户能够连续，被动和实时地进行身份验证。