Automated Inference on Criminality using Face Images.
Year: 2016
Type: preprint
Abstract: We study, for the first time, automated inference on criminality based solelyon still face images. Via supervised machine learning, we build four
classifiers (logistic regression, KNN, SVM, CNN) using facial images of 1856
real persons controlled for race, gender, age and facial expressions, nearly
half of whom were convicted criminals, for discriminating between criminals and
non-criminals. All four classifiers perform consistently well and produce
evidence for the validity of automated face-induced inference on criminality,
despite the historical controversy surrounding the topic. Also, we find some
discriminating structural features for predicting criminality, such as lip
curvature, eye inner corner distance, and the so-called nose-mouth angle. Above
all, the most important discovery of this research is that criminal and
non-criminal face images populate two quite distinctive manifolds. The
variation among criminal faces is significantly greater than that of the
non-criminal faces. The two manifolds consisting of criminal and non-criminal
faces appear to be concentric, with the non-criminal manifold lying in the
kernel with a smaller span, exhibiting a law of normality for faces of
non-criminals. In other words, the faces of general law-biding public have a
greater degree of resemblance compared with the faces of criminals, or
criminals have a higher degree of dissimilarity in facial appearance than
normal people.
Source: arXiv (Cornell University)
Authors: Xiaolin Wu, Xi Zhang
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2552893417