Human performance and next-generation facial identification technologies.

我们对人脸的感知和识别是生活的核心。我们的大脑不断地做出知觉判断，例如，一张脸是熟悉的，还是面部表情表明幸福。我们通常可以反思来评估我们在这些判断中的不确定性，归因于自信。自信归因被作为元认知的典型例子进行研究，反思我们的思想的能力被认为是意识的原型特征。最近，在人工智能(AI)驱动的合成材料(包括人脸)方面取得了巨大的技术进步。生成性对抗性网络(GANS)深度学习方法可以合成看起来逼真的虚拟人物的图像(图1)。这就提出了这样一个问题，即人类如何感知和反思真实面孔与人工智能生成的面孔，并促进在几个维度上(如相似性、种族)不同的大型刺激集，以测试面孔感知、识别和元认知理论，并识别它们的神经关联。联合研究人类经验和Gans-技术极大地推进了理论框架：新的感知现实监控账户提出，人类意识的计算操作类似Gans(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，如根据证人的记忆、身份游行和面部照片书开发面部合成。VisionMetric(https://visionmetric.com/)，是安全和警务部门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授权给英国约75%的警察队伍和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Gans生成的人脸的身份游行和面部照片书，并进一步开发系统来提取证人的记忆(例如，通过EEG)，而不依赖于他们的行为反应。这个项目结合了Visionmetric的工业级数据库和软件工具，以及Colloff和Bowman对人脸识别和神经熟悉度的理论理解，以回答关于人脸感知、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Gans人脸中识别出真正的人脸？2)什么因素(例如，人脸相似性；观察者和面孔的种族、性别或年龄)影响人类：(A)从Gans面孔中检测真实面孔？(B)对真实面孔和Gans面孔的判断(例如，可信度)？(C)当被一组Gans面孔包围时，先前看到的真实面孔的识别记忆？3)我们能否识别面孔熟悉度和信心的电生理标记物，以直接测量大脑的面孔识别记忆和自省？

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?