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首页> 外文期刊>Cognition: International Journal of Cognitive Psychology >Understanding face familiarity
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Understanding face familiarity

机译：理解面对熟悉程度

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It has been known for many years that identifying familiar faces is much easier than identifying unfamiliar faces, and that this familiar face advantage persists across a range of tasks. However, attempts to understand face familiarity have mostly used a binary contrast between 'familiar' and 'unfamiliar' faces, with no attempt to incorporate the vast range of familiarity we all experience. From family members to casual acquaintances and from personal to media exposure, familiarity is a more complex categorisation than is usually acknowledged. Here we model levels of familiarity using a generic statistical analysis (PCA combined with LDA) computed over some four thousand naturally occurring images that include a large variation in the numbers of images for each known person. Using a strong test of performance with entirely novel, untrained everyday images, we show that such a model can simulate widely documented effects of familiarity in face recognition and face matching, and offers a natural account of the internal feature advantage for familiar faces. Furthermore, as with human viewers, the benefits of familiarity seem to accrue from being able to extract consistent information across different photos of the same face. We argue that face familiarity is best understood as reflecting increasingly robust statistical descriptions of idiosyncratic within-person variability. Understanding how faces become familiar appears to rely on both bottom-up statistical image descriptions (modelled here with PCA), and top-down processes that cohere superficially different images of the same person (modelled here with LDA).
机译：已经知道多年来，识别熟悉的面孔比识别不熟悉的面孔更容易，并且这种熟悉的面部优势在一系列任务中仍然存在。然而，要了解面部熟悉的尝试大多使用“熟悉”和“不熟悉的面孔之间的二元对比，而没有尝试融入我们所有经验的广泛熟悉程度。从家庭成员到休闲熟人，从个人到媒体曝光，熟悉是比通常承认的更复杂的分类。在这里，我们使用通用统计分析（PCA与LDA组合）的熟悉程度计算超过了几四千个自然发生的图像，该图像包括每个已知人为的图像数量的大变化。使用强大的性能测试与完全小说，未经研磨的日常图像，我们表明这种模型可以模拟熟悉面部识别和面部匹配的熟悉程度，并提供了熟悉面部的内部特征优势的自然叙述。此外，与人类观看者一样，熟悉性的好处似乎累积能够在相同面的不同照片上提取一致的信息。我们认为，面对熟悉最受理解的是反映了对人内变异性的特殊内同步的越来越强大的统计描述。了解如何熟悉的脸部似乎依赖于自下而上的统计图像描述（这里与PCA建模），以及与同一人的过度不同的图像结合的自上而下的进程（这里用LDA建模）。

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