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

Anna Williams, H. Chang, C. Frowd
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Abstract

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.
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了解熟悉的人脸识别的3D扫描图像:内部和外部面部特征的重要性
粘土或计算机化的面部重建经常在没有头部外部特征(头发、耳朵和脖子)信息的情况下呈现给公众以供识别,因为这些被认为可能会分散注意力甚至误导。在这种背景下,人脸识别的机制尚不清楚,但现有的使用熟悉面孔照片的研究表明,外部特征在识别中起着重要作用,外部特征甚至可能是识别发生的必要条件。目前的研究旨在确定外部特征对3D图像中熟悉面孔的识别的贡献。它还将决定在重建中是否包含或排除外部特征可能是有益的。志愿者被要求说出他们认识的人的3D面部图像,这些图像要么是全脸的3D表面扫描,要么是内部或外部特征被删除的图像。正如预期的那样,在扫描中呈现的信息和识别率之间发现了明显的相关性,参与者正确命名全脸图像的频率最高,而外部特征图像的频率最低。Logistic回归分析表明,人脸的外部特征、内部特征和全脸特征的识别率呈显著的线性趋势。错误的命名也呈线性增加,这表明当提供更多有用的面部信息时,参与者更有可能提供一个名字(正确的或不正确的)。
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