  Judgments about personality based on facial appearance are strong effectors
in social decision making, and are known to have impact on areas from
presidential elections to jury decisions. Recent work has shown that it is
possible to predict perception of memorability, trustworthiness, intelligence
and other attributes in human face images. The most successful of these
approaches require face images expertly annotated with key facial landmarks. We
demonstrate a Convolutional Neural Network (CNN) model that is able to perform
the same task without the need for landmark features, thereby greatly
increasing efficiency. The model has high accuracy, surpassing human-level
performance in some cases. Furthermore, we use a deconvolutional approach to
visualize important features for perception of 22 attributes and demonstrate a
new method for separately visualizing positive and negative features.
