We can recognise people that we know across their lifespan. We see family members age, and we can recognise celebrities across long careers. How is this possible, despite the very large facial changes that occur as people get older? Here we analyse the statistical properties of faces as they age, sampling photos of the same people from their 20s to their 70s. Across a number of simulations, we observe that individuals' faces retain some idiosyncratic physical properties across the adult lifespan that can be used to support moderate levels of age-independent recognition. However, we found that models based exclusively on image-similarity only achieved limited success in recognising faces across age. In contrast, more robust recognition was achieved with the introduction of a minimal top-down familiarisation procedure. Such models can incorporate the within-person variability associated with a particular individual to show a surprisingly high level of generalisation, even across the lifespan. The analysis of this variability reveals a powerful statistical tool for understanding recognition, and demonstrates how visual representations may support operations typically thought to require conceptual properties.

中文翻译：

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整个生命周期的面部识别

我们可以识别出我们一生中认识的人。我们看到家庭成员变老，我们可以认出长期职业生涯中的名人。尽管随着年龄的增长会发生非常大的面部变化，这怎么可能？在这里，我们分析人脸随着年龄增长的统计特性，对 20 多岁到 70 多岁的同一个人的照片进行抽样。在许多模拟中，我们观察到个体的面部在整个成年生命周期中保留了一些特殊的物理特性，可用于支持中等水平的与年龄无关的识别。然而，我们发现完全基于图像相似性的模型在识别不同年龄段的人脸方面取得的成功有限。相比之下，通过引入最小的自上而下的熟悉程序，实现了更强大的识别。这样的模型可以结合与特定个体相关的人内可变性，以显示出惊人的高泛化水平，甚至在整个生命周期中也是如此。对这种可变性的分析揭示了一种用于理解识别的强大统计工具，并展示了视觉表示如何支持通常被认为需要概念属性的操作。