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Deep convolutional neural networks in the face of caricature
Nature Machine Intelligence ( IF 23.8 ) Pub Date : 2019-11-12 , DOI: 10.1038/s42256-019-0111-7
Matthew Q. Hill , Connor J. Parde , Carlos D. Castillo , Y. Ivette Colón , Rajeev Ranjan , Jun-Cheng Chen , Volker Blanz , Alice J. O’Toole

Real-world face recognition requires us to perceive the uniqueness of a face across variable images. Deep convolutional neural networks (DCNNs) accomplish this feat by generating robust face representations that can be analysed in a multidimensional ‘face space’. We examined the organization of viewpoint, illumination, gender and identity in this space. We found that DCNNs create a highly organized face similarity structure in which identities and images coexist. Natural image variation is organized hierarchically, with face identity nested under gender, and illumination and viewpoint nested under identity. To examine identity, we caricatured faces and found that identification accuracy increased with the strength of identity information in a face, and caricature representations ‘resembled’ their veridical counterparts—mimicking human perception. DCNNs therefore offer a theoretical framework for reconciling decades of behavioural and neural results that emphasized either the image or the face in representations, without understanding how a neural code could seamlessly accommodate both.

A preprint version of the article is available at ArXiv.


中文翻译:

面对漫画的深度卷积神经网络

现实世界中的人脸识别要求我们在可变图像中感知人脸的唯一性。深度卷积神经网络(DCNN)通过生成可以在多维“人脸空间”中进行分析的鲁棒人脸表示来实现这一壮举。我们检查了该空间中视点,照明,性别和身份的组织。我们发现DCNN创建了高度组织的面部相似结构,其中身份和图像共存。自然图像的变化是分层组织的,面部身份嵌套在性别之下,照明和视点嵌套在身份之下。为了检查身份,我们对面孔进行了漫画化,发现识别准确性随着面孔中身份信息的强度而增加,并且漫画表示“类似于”他们的垂直对应物,从而模仿了人类的感知。

该文章的预印本可在ArXiv上获得。
更新日期:2020-01-14
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