Physiognomy: Personality Traits Prediction by Learning附视频

被引:2
作者
Ting Zhang [1 ,2 ]
RiZhen Qin [1 ]
QiuLei Dong [1 ,3 ,2 ]
Wei Gao [1 ]
HuaRong Xu [4 ]
ZhanYi Hu [1 ,3 ,2 ]
机构
[1] National Laboratory of Pattern Recognition, Institute of Automation,Chinese Academy of Sciences
[2] University of Chinese Academy of Sciences
[3] Center for Excellence in Brain Science and Intelligence Technology,Chinese Academy of Sciences
[4] Department of Computer Science & Technology, Xiamen Institute of
关键词
Personality traits; physiognomy; face image; deep learning; convolutional neural network(CNN);
D O I
暂无
中图分类号
TP183 [人工神经网络与计算]; TP391.41 [];
学科分类号
080203 ;
摘要
Evaluating individuals personality traits and intelligence from their faces plays a crucial role in interpersonal relationship and important social events such as elections and court sentences. To assess the possible correlations between personality traits(also measured intelligence) and face images, we first construct a dataset consisting of face photographs, personality measurements,and intelligence measurements. Then, we build an end-to-end convolutional neural network for prediction of personality traits and intelligence to investigate whether self-reported personality traits and intelligence can be predicted reliably from a face image. To our knowledge, it is the first work where deep learning is applied to this problem. Experimental results show the following three points: 1)"Rule-consciousness" and "Tension" can be reliably predicted from face images. 2) It is difficult, if not impossible, to predict intelligence from face images, a finding in accord with previous studies. 3) Convolutional neural network(CNN) features outperform traditional handcrafted features in predicting traits.
引用
收藏
页码:386 / 395
页数:10
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