Human performance and next-generation facial identification technologies.

人类表现和下一代面部识别技术。

• 批准号: 2884534
• 金额: --
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2884534
• 关键词: Human; performance; next; generation; facial

Our perception and recognition of human faces is central to life. Our brains continuously make perceptual judgements, e.g. whether a face is familiar or a facial expression indicates happiness. We can generally introspect to assess our uncertainty in these judgements, attributing confidence. Confidence attribution is studied as a canonical example of metacognition, with the capacity to reflect on our thoughts considered an archetypal characteristic of consciousness.Recently, there have been huge technological advances in Artificial Intelligence (AI)-powered synthesised material, including faces. The Generative Adversarial Networks (GANs) deep-learning method can synthesise images of realistic-looking fictional people (Fig1). This raises questions about how humans perceive and introspect about real versus AI-generated faces, and facilitates large stimuli sets that can vary on several dimensions (e.g., similarity, ethnicity) for testing theories of face perception, recognition, and metacognition, and identifying their neural correlates.Jointly studying human experiences and GANs-technologies vastly advances theoretical frameworks: New perceptual reality monitoring accounts propose that human consciousness operates computationally like GANs (Lau, 2019). Joint study also opens new avenues for practical application in multiple domains, including forensics such as the development of face-composites from witnesses' memory, identity-parades,and mugshot books. Visionmetric (https://visionmetric.com/), is a successful micro-SME in the Security and Policing sector, best known for its innovative facial-composite system EFIT6. EFIT6 is licensed to ~75% of UK police constabularies, and law enforcement agencies in 30 countries. Visionmetric wants to expand their product-base to include identity-parades and mug-shot books using GANs-generated faces, and further develop systems to extract memories from a witness (e.g.,via EEG) without relying on their behavioural response.This project combines Visionmetric's industry-grade databases and software tools, with Colloff and Bowman's theoretical understanding of face recognition and neural-correlates of familiarity to answer important theoretical and applied questions about face perception, recognition, and introspection:1) Can humans detect a real face from a group of simultaneously presented GANs faces?2) What factors (e.g., face similarity; ethnicity, gender or age of the observer and the faces) influence human:(a) detection of real faces from GANs faces?(b) judgements of real and GANs faces (e.g., trustworthiness)?(c) recognition memory for a previously seen real face when surrounded by a group of GANs faces?3) Can we identify electrophysiological markers of face familiarity and confidence to directly measure face recognition memory and introspection from the brain?

我们对人脸的感知和识别是生活的核心。我们的大脑不断做出感知判断，例如，一张脸是否熟悉，或者面部表情是否表示幸福。我们通常可以通过内省来评估我们在这些判断中的不确定性，归因于信心。信心归因是作为元认知的一个典型例子进行研究的，反映我们思想的能力被认为是意识的原型特征。最近，人工智能（AI）驱动的合成材料（包括人脸）取得了巨大的技术进步。生成对抗网络（GANs）深度学习方法可以合成逼真的虚构人物的图像（图1）。这提出了关于人类如何感知和内省真实的与AI生成的面部的问题，并促进了可以在多个维度上变化的大刺激集（例如，共同研究人类经验和GANs技术极大地推进了理论框架：新的感知现实监测账户提出，人类意识像GANs一样在计算上运行（Lau，2019）。联合研究还为多个领域的实际应用开辟了新的途径，包括法医学，如从证人的记忆，身份游行和面部照片书籍中开发面部合成材料。Visionmetric（https：//visionmetric.com/）是安全和警务领域一家成功的微型中小企业，以其创新的面部复合系统EFIT 6而闻名。EFIT 6已授权给约75%的英国警察局和30个国家的执法机构。Visionmetric希望扩大他们的产品基础，包括使用GAN生成的面部的身份游行和面部照片，并进一步开发从证人中提取记忆的系统（例如，该项目结合了Visionmetric的工业级数据库和软件工具，以及Colloff和Bowman对人脸识别和熟悉度的神经相关性的理论理解，以回答有关人脸感知，识别和内省的重要理论和应用问题：1）人类能否从一组同时呈现的GAN人脸中检测出真实的人脸？2)哪些因素（例如，面部相似性;观察者和面部的种族、性别或年龄）影响人类：（a）从GAN面部检测真实的面部？(b)对真实的和GAN面部的判断（例如，可信度）？(c)当被一组GAN人脸包围时，对先前看到的真实的人脸的识别记忆？3)我们能否识别出面孔熟悉度和自信心的电生理标记，从而直接测量大脑对面孔的识别、记忆和内省？