Two sources of bias explain errors in facial age estimation.

Two sources of bias explain errors in facial age estimation.
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DOI:
10.1098/rsos.180841
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发表时间:
2018-10
影响因子:
3.5
通讯作者:
White D
White D
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Clifford CWG;Watson TL;White D

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准确的年龄估计是我们日常社交、提供年龄限制服务和警方调查的基础。以前的工作表明,这些判断很容易出错,但导致这些错误的过程尚不清楚。在这里,我们首次系统地测试了年龄估计中的偏差,使用了一个不同年龄的标准化护照图像的大型数据库(n=3948)。在三个实验中,我们测试了一系列感知年龄组(n=84),发现平均年龄估计误差约为8岁。我们发现,这种误差可以归因于两个可分离的偏向来源。首先,我们的结果显示了一种同化的序列相依性,因此估计值系统性地偏向于前一张脸的年龄,这是对绝大多数方差的解释。其次,年轻的面孔通常被认为比实际年龄更老,而年长的面孔则更年轻。这些偏差加在一起,约占年龄估计偏差的95%。我们的结论是,对年龄的感知是由编码观看者最近和正常接触面孔的表征来调节的。基于我们的直接经验,年龄知觉受到自上而下的强烈影响,这一发现对于我们理解涉及面孔知觉的知觉过程,以及在重要的现实世界任务中提高年龄估计的准确性具有重要意义。
Accurate age estimates underpin our everyday social interactions, the provision of age-restricted services and police investigations. Previous work suggests that these judgements are error-prone, but the processes giving rise to these errors are not understood. Here, we present the first systematic test of bias in age estimation using a large database of standardized passport images of heterogeneous ages (n = 3948). In three experiments, we tested a range of perceiver age groups (n = 84), and found average age estimation error to be approximately 8 years. We show that this error can be attributed to two separable sources of bias. First, and accounting for the vast majority of variance, our results show an assimilative serial dependency whereby estimates are systematically biased towards the age of the preceding face. Second, younger faces are generally perceived to be older than they are, and older faces to be younger. In combination, these biases account for around 95% of variance in age estimates. We conclude that perception of age is modulated by representations that encode both a viewer's recent and normative exposure to faces. The finding that age perception is subject to strong top-down influences based on our immediate experience has implications for our understanding of perceptual processes involved in face perception, and for improving accuracy of age estimation in important real-world tasks.
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