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Understanding Familiar Face Recognition for 3D Scanned Images: The Importance of Internal and External Facial Features

机译:了解3D扫描图像的熟悉面部识别:内部和外部面部特征的重要性

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Clay or computerised facial reconstructions are often presented to the public for recognition without information about the external features of a head (hair, ears and neck) as these are thought to be potentially distracting or even misleading. In this context, the mechanisms of face recognition are poorly understood, but existing research using photographs of familiar faces suggests that external features play an important role for recognition, and external features may even be necessary for recognition to occur at all. The current research aimed to determine the contribution that external features make to the recognition of a familiar face rendered in 3D. It will also determine whether the inclusion or exclusion of external features from a reconstruction is likely to be beneficial. Volunteers were asked to name images of 3D faces of people known to them, presented as either full face 3D surface scans, or where the internal or external features had been removed. As was expected, a clear correlation was found between information presented in the scans and the recognition rate, with participants correctly naming full face images most often and images of external features least often. Logistic regression analysis demonstrated a significant linear trend in recognition rate in the order of external features, internal features and full face. Incorrect naming also increased linearly, indicating that participants were more likely to offer a name (correct or otherwise) when more useful facial information was provided.
机译:黏土或计算机化的面部重建通常会在没有任何有关头部(头发,耳朵和脖子)外部特征的信息的情况下向公众展示,因为这些特征可能会分散注意力甚至引起误解。在这种情况下,人脸识别的机制了解甚少,但是现有的使用熟悉的人脸照片的研究表明,外部特征在识别中起着重要作用,甚至根本不需要外部特征来进行识别。当前的研究旨在确定外部特征对识别3D渲染的熟悉面孔的贡献。它还将确定在重建中包含或排除外部特征是否可能是有益的。要求志愿者命名他们所认识的人的3D面部图像,并以全脸3D表面扫描或内部或外部特征被移除的位置显示。不出所料,在扫描中显示的信息与识别率之间发现了明显的相关性,参与者最常正确命名全脸图像,而最不频繁地命名外部特征图像。 Logistic回归分析显示出识别率的显着线性趋势按外部特征,内部特征和全脸的顺序排列。错误的命名也呈线性增加,表明当提供更多有用的面部信息时,参与者更有可能提供名字(正确或其他)。

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