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，例如无论是熟悉的面孔还是面部表情都表明幸福。我们通常可以通过内省来评估我们对这些判断的不确定性，从而赋予信心。信心归因被作为元认知的典型例子来研究，它具有反思我们思想的能力，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（GAN）深度学习方法可以合成逼真的虚构人物的图像（图 1）。这提出了人类如何感知和反思真实面孔与人工智能生成面孔的问题，并促进了可以在多个维度（例如相似性、种族）上变化的大型刺激集，以测试面孔感知、识别和元认知理论，并识别它们的神经相关性。联合研究人类经验和 GAN 技术极大地推进了理论框架： 感知现实监测表明，人类意识的计算方式类似于 GAN（Lau，2019）。联合研究还为多个领域的实际应用开辟了新的途径，包括取证学，例如根据目击者记忆、身份游行和面部照片簿开发面部合成材料。 Visionmetric (https://visionmetric.com/) 是安全和警务领域一家成功的微型中小企业，以其创新的面部合成系统 EFIT6 而闻名。 EFIT6 已获得约 75% 的英国警察局以及 30 个国家/地区执法机构的许可。 Visionmetric 希望扩大其产品基础，包括使用 GAN 生成的面孔进行身份游行和面部照片书籍，并进一步开发系统来提取证人的记忆（例如，通过脑电图），而不依赖于他们的行为反应。该项目结合了 Visionmetric 的工业级数据库和软件工具，以及 Colloff 和 Bowman 对人脸识别的理论理解和熟悉度的神经关联，以回答有关人脸的重要理论和应用问题 感知、识别和内省：1) 人类能否从一组同时呈现的 GAN 面孔中检测到真实面孔？2) 哪些因素（例如，面孔相似性；观察者和面孔的种族、性别或年龄）影响人类：(a) 从 GAN 面孔中检测真实面孔？(b) 对真实面孔和 GAN 面孔的判断（例如可信度）？(c) 识别记忆 以前被一组 GAN 面孔包围时看到过真实的面孔吗？3）我们能否识别面孔熟悉度和置信度的电生理标记，以直接测量大脑的面孔识别记忆和内省？