Face dissimilarity judgments are predicted by representational distance in morphable and image-computable models

Author:

Jozwik Kamila M.1ORCID,O’Keeffe Jonathan2,Storrs Katherine R.34ORCID,Guo Wenxuan56ORCID,Golan Tal5ORCID,Kriegeskorte Nikolaus5678ORCID

Affiliation:

1. Department of Psychology, University of Cambridge, Cambridge CB2 3EB, United Kingdom

2. MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, United Kingdom

3. Department of Experimental Psychology, Justus Liebig University, 35394 Giessen, Germany

4. Centre for Mind, Brain and Behaviour, University of Marburg, Justus Liebig University Giessen, 35394 Giessen, Germany

5. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027

6. Department of Psychology, Columbia University, New York, NY 10027

7. Department of Neuroscience, Columbia University, New York, NY 10027

8. Department of Electrical Engineering, Columbia University, New York, NY 10027

Abstract

Human vision is attuned to the subtle differences between individual faces. Yet we lack a quantitative way of predicting how similar two face images look and whether they appear to show the same person. Principal component–based three-dimensional (3D) morphable models are widely used to generate stimuli in face perception research. These models capture the distribution of real human faces in terms of dimensions of physical shape and texture. How well does a “face space” based on these dimensions capture the similarity relationships humans perceive among faces? To answer this, we designed a behavioral task to collect dissimilarity and same/different identity judgments for 232 pairs of realistic faces. Stimuli sampled geometric relationships in a face space derived from principal components of 3D shape and texture (Basel face model [BFM]). We then compared a wide range of models in their ability to predict the data, including the BFM from which faces were generated, an active appearance model derived from face photographs, and image-computable models of visual perception. Euclidean distance in the BFM explained both dissimilarity and identity judgments surprisingly well. In a comparison against 16 diverse models, BFM distance was competitive with representational distances in state-of-the-art deep neural networks (DNNs), including novel DNNs trained on BFM synthetic identities or BFM latents. Models capturing the distribution of face shape and texture across individuals are not only useful tools for stimulus generation. They also capture important information about how faces are perceived, suggesting that human face representations are tuned to the statistical distribution of faces.

Funder

Wellcome Trust

Alexander von Humboldt-Stiftung

Charles H. Revson Foundation

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

Cited by 20 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3