Learning to Recognize Faces Despite Within-Person Variability in Appearance: A Developmental Approach

识别人脸是一项挑战：同一个人的两张照片可能看起来非常不同，而两个人的照片可能看起来非常相似。尽管如此，成年人可以识别出成千上万张面孔，尽管每个人的外表都有差异我们依靠这种能力在忙碌火车站找到朋友，跟随电影情节，和邻居打招呼。这种识别熟悉面孔的技能创造了对所有面孔的专业知识的错觉，证明了在涉及不熟悉面孔的设置中使用照片ID的合理性（例如，在获得医疗保健或购买年龄限制商品时;边境安全）。当被问及陌生面孔的照片是属于同一个人还是两个不同的人时，成年人会犯很多错误;灯光或表情的简单变化可能会被误解为身份的变化。大量的研究正在研究成年人对陌生面孔和熟悉面孔的心理表征，以及一张新面孔变得熟悉的过程。机器学习的重大进展为人脸识别的理论模型提供了信息。 发展研究对于丰富我们对成年人专业知识的理解、改进机器学习和识别儿童犯错的条件至关重要。我发现，为陌生面孔匹配身份的能力在童年时期有一个长期的发展，就像学习新遇到的面孔的能力一样。到6岁时，但不是更小的时候，孩子们可以轻松地识别非常熟悉的面孔（他们的父母/老师）。通过最先进的机器学习算法，我的建议包括三个项目，以研究儿童时期面部学习和识别的长期发展。 项目A使用头戴式摄像机来发现儿童在与成人和同龄人互动时对面部（与物体）的注意力的年龄相关变化。我将研究经验是否会影响儿童在不熟悉的面孔中匹配身份和识别高度熟悉的面孔的能力。项目B侧重于对关键特征（例如，嘴唇厚度、眼睛颜色）--这些特征决定了成年人对陌生和熟悉面孔的身份认知。我将研究儿童是否比成人对关键特征不那么敏感，以及儿童与成人的面部关键特征是否不同。项目C将研究情境线索对儿童面孔学习和识别的影响。我将发现儿童是否比成年人更受益于传记信息，在群体中学习面孔，并在他们学习的背景下看到熟悉的面孔。 研究结果将提供有关面部识别在儿童时期如何进行微调的新见解，为理论模型提供信息，并导致更好的人工智能。

Recognizing faces is challenging: Two pictures of the same person can look very different and pictures of two people can look very similar. Nonetheless, adults can recognize thousands of faces despite within-person variability in appearance-an ability we rely on to find a friend in a busy train station, follow movie plots, and greet neighbours. This skill for recognizing familiar faces creates the illusion of expertise for all faces, justifying the use of photo ID in settings that involve unfamiliar faces (e.g., when accessing health care or buying age-restricted goods; border security). Adults make many errors when asked whether photos of unfamiliar faces belong to the same person or two different people; a simple change in lighting or expression can be misperceived as a change in identity. A spate of research is examining adults' mental representation of unfamiliar vs. familiar faces and the processes by which a newly encountered face becomes familiar. Significant advances in machine learning have informed theoretical models of face recognition. Developmental research is essential to enrich our understanding of adults' expertise, improve machine learning, and identify conditions under which children make errors. I discovered that matching identity for unfamiliar faces has a prolonged development during childhood, as does the ability to learn a newly encountered face. By 6 years of age, but not younger, children recognize highly familiar faces (their parent/teacher) with ease. Informed by state-of-the-art machine learning algorithms, my proposal comprises three projects to examine the prolonged development of face learning and recognition during childhood. Project A uses head-mounted cameras to discover age-related changes in children's attention to faces (vs. objects) when interacting with adults and peers. I will examine whether experience impacts children's ability to match identity in unfamiliar faces and recognize highly familiar faces. Project B focuses on age-related changes in sensitivity to critical features (e.g., lip thickness, eye colour)-features that govern adults' perception of identity in both unfamiliar and familiar faces. I will examine whether children are less sensitive to critical features than adults and whether critical features differ for child vs. adult faces. Project C will examine the influence of contextual cues on children's face learning and recognition. I will discover whether children benefit more than adults from biographical information, learning faces in groups, and seeing familiar faces in the context in which they were learned. The results will provide novel insights about how face recognition becomes finely tuned during childhood, informing theoretical models and leading to better artificial intelligence.