  The human behavior of evaluating other individuals with respect to their
personality traits and intelligence by evaluating their faces plays a crucial
role in human relations. These trait judgments might influence important social
outcomes in our lives such as elections and court sentences. Previous studies
have reported that human can make valid inferences for at least four
personality traits. In addition, some studies have demonstrated that facial
trait evaluation can be learned using machine learning methods accurately. In
this work, we experimentally explore whether self-reported personality traits
and intelligence can be predicted reliably from a facial image. More
specifically, the prediction problem is separately cast in two parts: a
classification task and a regression task. A facial structural feature is
constructed from the relations among facial salient points, and an appearance
feature is built by five texture descriptors. In addition, a minutia-based
fingerprint feature from a fingerprint image is also explored. The
classification results show that the personality traits "Rule-consciousness"
and "Vigilance" can be predicted reliably, and that the traits of females can
be predicted more accurately than those of male. However, the regression
experiments show that it is difficult to predict scores for individual
personality traits and intelligence. The residual plots and the correlation
results indicate no evident linear correlation between the measured scores and
the predicted scores. Both the classification and the regression results reveal
that "Rule-consciousness" and "Tension" can be reliably predicted from the
facial features, while "Social boldness" gets the worst prediction results. The
experiments results show that it is difficult to predict intelligence from
either the facial features or the fingerprint feature, a finding that is in
agreement with previous studies.

Computer Vision and Pattern Recognition