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Two-Factor Authentication Using Leap Motion and Numeric Keypad

机译:使用Leap Motion和Numeric键盘的双因素身份验证

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Biometric authentication has become popular in modem society. It takes less time and effort for users when compared to conventional password authentication. Furthermore, biometric authentication was considered more secure than password authentication because it was more difficult to steal biometric information when compared to passwords. However, given the development of high-spec cameras and image recognition technology, the risk of the theft of biometric information, such as fingerprints, is increasing. Additionally, biometric authentication exhibits lower and less stable accuracy than that of password authentication. To solve the aforementioned issues, we propose two-factor authentication combining password-input and biometric authentication of the hand. We adopt Leap Motion to measure physical and behavioral features related to hands. Subsequently, a random forest classifier determines whether the hand features belongs to a genuine user. Our authentication system architecture completes the biometric authentication by using a limited amount of data obtained within a few seconds when a user enters a password. The advantage of the proposed method is that it prevents intrusion by biometric authentication even if a password is stolen. Our experimental results for 21 testers exhibit 94.98% authentication accuracy in a limited duration, 2.52 s on an average while inputting a password.
机译:生物识别身份验证在调制解调器社会中变得流行。与传统密码认证相比,用户需要更少的时间和精力。此外,生物识别身份验证被认为比密码认证更安全,因为与密码相比,窃取生物信息更难。然而,鉴于高规格摄像机和图像识别技术的发展,盗窃生物识别信息(例如指纹)的风险正在增加。此外,生物识别身份验证呈现比密码认证更低且较低稳定的准确性。要解决上述问题,我们提出了双因素认证,组合了手的密码输入和生物识别身份验证。我们采用跨运动来衡量与手相关的物理和行为特征。随后,随机林分类器确定手部特征是否属于真正的用户。我们的认证系统架构通过使用在用户进入密码时几秒钟内获得的有限数量的数据完成生物识别认证。所提出的方法的优点是它即使密码被盗,它也可以通过生物识别认证来防止入侵。我们的21台测试仪的实验结果表现出94.98%的认证精度,在有限的持续时间内,平均在输入密码时平均验证精度为2.52秒。

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