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人类和人工神经网络中面部的视图对称表示

Xun Zhu¹, David M Watson¹, Daniel Rogers¹

¹Department of Psychology, University of York, YO10 4PF, UK.

Neuropsychologia

|December 7, 2024

概括

深度神经网络中的视觉对称性反映了人类面部识别. 一个深层卷积神经网络 (DCNN) 显示了头部曲折的视图对称表示,类似于人类的感知,有助于识别身份.

科学领域:

认知神经科学认知神经科学
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

视图对称是人类大脑中面部识别的中介表示方式.
了解人工系统如何处理面部视图,可以阐明人类视觉处理.

研究的目的:

为了比较人类和深层卷积神经网络 (DCNNs) 的视图对称表示.
研究DCNN在面部视图变化 (yaw,pitch,roll) 期间出现视图对称性的情况.
将DCNN视图对称响应与人类行为和神经数据相关联.

主要方法:

一个经过面部识别训练的DCNN被分析了跨层次的代表性变化.
对比DCNN输出与头部旋转在曲率,曲率和滚动.
收集了人类参与者的行为判断和神经反应,并与DCNN发现进行了比较.

主要成果:

从视图特定到视图对称表示的过渡发生在DCNN完全连接的层中.
这种视图对称的表示是依赖于身份的,对于相同的身份来说更强大.
在DCNN中,没有观察到DCNN中杆或滚轮旋转的类似过渡.
DCNN对的视图对称反应与人类感知相似性判断和更高的视觉皮层活动相关.

结论:

视图对称性在DCNN中出现,用于头部曲折,特别是当旋转创建镜像时.
DCNN可以模拟人类面部感知方面的方面,这表明视图对称性是一个计算高效的策略.
这些发现支持视图对称在人造和人脸识别系统中的作用.

关键词:

DCNN DCN 在线网络面对面的面对面的面对面的面对面对称性对称性对称性对称性对称性视角观点观点功能磁力共振成像 (fMRI) 是一种

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