Hummingbird: Human-machine integration for biometric authentication

我们生活在一个科技时代，我们可以用声音作为密码访问网上银行，我们的孩子可以用指纹支付学校午餐。生物识别技术反映了我们的生理或行为特征，现在作为一种证明我们身份的方式很常见，以便获得安全的信息、服务或空间。鉴于与生物识别技术相关的重要用途，为了鼓励公民和服务提供者之间的信任，生物识别分析的准确性是一个基本需求。在HUMMINGBIRD项目中进行的可行性研究将提供一个以人为本的框架来解决这两个需求。最近两份高规格报告的发表使这一努力显得及时和必要。首先是英国政府对“未来身份”的审查，其中认识到数字技术对身份的变革性影响。它特别指出，我们现在有无数种方式来传达我们的身份，并让它被欺骗。第二个是英国议会特别委员会对“生物识别技术的当前和未来用途”的审查，其中强调了生物识别分析的两个必要的未来步骤：分析应该利用行为和生理措施；它应该充分利用多种生物识别技术和决策者之间的数据组合，以改善决策。为了解决所有因素，我们提出了一个令人兴奋的项目，该项目将提供一个以人为灵感的多专家、多模态的生物识别分析框架。这将满足三个目标：首先，它将通过结合人类使用的成功策略，为自动生物识别分析提供增强的算法。其次，它将提供一种方法，将人类做出的决定与（增强的）算法相结合，以提高准确性。第三，它将提供结合多种生物识别技术的潜力，在单一模式可能不理想的情况下提供弹性。HUMMINGBIRD项目团队拥有独特的技能组合来探索这个想法，事实上，我们建立在最近发表的关于这个主题的理论工作之上。在本提案中，我们研究了两种生物特征-面部和声音-这反映了将静态生理测量（面部图像）和动态行为测量（时间语音样本）相结合的举动。我们还专注于两个决策者-人类和最先进的自动算法-提供与人类必须成为决策过程一部分的场景直接相关的场景（例如在法医决策中）。我们的工作将建立人类和机器算法在最佳和次最佳呈现条件下识别人脸和声音时的基本性能水平。然后，它将寻求通过结合人类规则和启发式来增强机器算法。这样的举措通过简化自动化解决方案提供了提高准确性和效率的潜力。更重要的是，它利用了人类在某些条件下可以胜过机器算法的事实，比如在昏暗的灯光下识别人脸，或者在噪音中识别声音。最后，我们的工作将应用一个创新的数据融合模型来结合人类和机器算法的决策，从一个生物特征，然后从多个生物特征。我们工作中的这种新颖和创造性元素解决了在情况本身不确定的情况下准确性、分歧解决和对身份决定的最终信心等问题。可以说，生物识别技术比令牌或密码系统更直接地反映身份，因为它们依赖于我们是谁，而不是我们拥有或知道什么。因此，生物识别分析可能仍然是身份管理的支柱。HUMMINGBIRD项目展示了一种真正的希望，它可以提高身份分析的准确性和信心，随着技术的进步，实现身份管理的准确性和可信度。

We live in a technological age in which we can use our voice as a password to access online banking, and our children can pay for school lunches with a fingerprint. Biometrics, which reflect our physiological or behavioural characteristics, are now common as a way to prove our identity in order to access secure information, services or spaces. Given the important uses associated with biometrics, there is a fundamental need for accuracy in biometric analysis in order to encourage trust amongst both citizens and service providers. The feasibility study undertaken within the HUMMINGBIRD project will provide a human-inspired framework to address both needs.The recent publication of two high profile report converge to make this endeavour timely and necessary. The first is the UK Governmental review on 'Future Identities', which recognised the transformative effect that digital technologies are having on identity. In particular, it noted the myriad of ways we now have to convey our identity, and to have it spoofed. The second is the UK Parliamentary Select Committee review on 'The Current and Future Uses of Biometrics' which highlighted two necessary future steps for biometric analysis: Analysis should draw on behavioural as well as physiological measures; and it should take full advantage of the combination of data across multiple biometrics and across decision makers in order to improve decision-making.To address all factors, we propose an exciting project that will deliver a human-inspired multi-expert, multi-modality framework for biometric analysis. This will satisfy three aims: First, it will deliver enhanced algorithms for automated biometric analysis by incorporating successful strategies used by humans. Second, it will deliver a method of combining decisions made by humans and (enhanced) algorithms in order to boost accuracy. Third, it will deliver the potential to combine multiple biometrics, providing resilience in scenarios in which a single modality may be sub-optimal.The HUMMINGBIRD project team possesses a unique combination of skills to explore this idea and indeed, we build on recently published theoretical work on this topic. In this proposal, we examine two biometrics - face and voice - which reflects the move to combine static physiological measures (facial images) and dynamic behavioural measures (temporal voice samples). We also concentrate on two decision-makers - the human and state-of-the-art automated algorithm - providing direct relevance to scenarios in which the human must be part of the decision-process (such as in forensic decisions). Our work will establish the fundamental performance levels of humans and machine algorithms when recognising faces and voices under optimal and sub-optimal presentation conditions. It will then seek to enhance the machine algorithms through incorporation of human rules and heuristics. Such a move offers the potential to boost accuracy and efficiency by streamlining automated solutions. More importantly, it exploits the fact that humans can outperform machine algorithms under some conditions, such as when trying to recognise a face under dim light, or a voice amidst noise. Finally, our work will apply an innovative data fusion model to combine the decisions of humans and machine algorithms from one biometric, and then from multiple biometrics. This novel and creative element of our work addresses issues of accuracy, disagreement resolution, and resultant confidence in an identity decision, when the situation is inherently uncertain. Arguably, biometrics reflect identity more directly than token or password systems because they rely on who we are rather than what we have or know. As such, biometric analysis is likely to remain a mainstay of identity management. The HUMMINGBIRD project presents real promise as a way to improve accuracy and confidence in that analysis, enabling accuracy of, and trust in, identity management as technology advances.