Functional Biometrics – Using body-reflections as a novel class of biometric authentication systems

功能生物识别 – 使用身体反射作为一类新型生物识别认证系统

• 批准号: 426052422
• 负责人: Professor Dr. Stefan Schneegass
• 金额: --
• 依托单位: Arbeitsgruppe Mensch-Computer Interaktion
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/426052422?language=en
• 关键词: Functional; Biometrics; Using; body; reflections

The number of systems requiring user authentication is increasing every year. Classical authentication approaches such as Personal Identification Numbers (PINs) and passwords overwhelm users since they have to remember dozens of them. Research also showed that the selection of user-generated passwords is predictable. To counteract these weaknesses, devices such as smartphones and laptops are more often equipped with biometric authentication. The most common biometric authentication approaches are fingerprints or face recognition, which use the body of a user as a physical token for identification. While these approaches currently provide a sufficient level of security, they have two inherent drawbacks: (1) the user is not able to change biometric passwords and (2) the user is leaving them essentially everywhere (e.g., leaving fingerprints by touching the environment, video surveillance cameras recording the user´s head and body). To tackle these challenges, we introduce a novel class of biometrics, called Functional Biometrics. In contrast to physical token and behavior based biometrics, Functional Biometrics exploit the user´s body as a function. Thus, this novel class of biometrics does not only rely on the body itself but also on a specific input signal generated by the system the user wants to authenticate to. This signal (e.g., audio, electrical, haptic stimulus) is modified by the user´s body, which in return generates a characteristic response through the user’s unique body reflection (e.g., propagated audio response, muscle reaction). This characteristic response operates as a biometric password. The system measures the response and compares it to a pre-stored response (i.e., the password) to authenticate the user. Functional Biometrics combine the advantages of common knowledge-based authentication approaches such as alphanumeric passwords (i.e., changeable, a multitude of passwords per user) with the advantages of biometrics authentication approaches such as fingerprints (i.e., no cognitive load and no memorability required). In this project, we will systematically explore the design space of functional biometrics. We will investigate the feasibility of different sensing and actuation technologies, develop and adapt models and algorithms for automatically authenticate users based on measured body reflection, and will develop research probes and an authentication framework to synthesize the gained knowledge.

需要用户身份验证的系统数量每年都在增加。传统的身份验证方法，如个人识别码（PIN）和密码，使用户不堪重负，因为他们必须记住几十个密码。研究还表明，用户生成的密码的选择是可预测的。为了克服这些弱点，智能手机和笔记本电脑等设备更经常配备生物识别认证。最常见的生物特征认证方法是指纹或面部识别，它们使用用户的身体作为身份识别的物理令牌。虽然这些方法目前提供了足够的安全级别，但它们具有两个固有的缺点：（1）用户不能改变生物特征密码，以及（2）用户基本上将它们留在任何地方（例如，通过触摸环境留下指纹，视频监控摄像头记录用户的头部和身体）。为了应对这些挑战，我们引入了一类新的生物识别技术，称为功能生物识别技术。与基于物理令牌和行为的生物识别技术相比，功能生物识别技术利用用户的身体作为功能。因此，这种新型的生物识别技术不仅依赖于身体本身，而且还依赖于用户想要认证的系统生成的特定输入信号。该信号（例如，音频、电、触觉刺激）被用户的身体修改，用户的身体反过来通过用户独特的身体反射产生特征响应（例如，传播的音频反应、肌肉反应）。该特征响应作为生物特征密码操作。系统测量响应并将其与预先存储的响应（即，密码）来验证用户。功能性生物测定联合收割机结合了基于常识的认证方法的优点，例如字母数字密码（即，可改变的，每个用户的多个密码），具有诸如指纹的生物测定认证方法的优点（即，不需要认知负荷和记忆力）。在这个项目中，我们将系统地探索功能生物识别的设计空间。我们将研究不同的传感和驱动技术的可行性，开发和调整模型和算法，根据测量的身体反射自动认证用户，并将开发研究探针和认证框架，以综合所获得的知识。