  This article is a sequel to our earlier work [25]. The main objective of our
research is to explore the potential of supervised machine learning in
face-induced social computing and cognition, riding on the momentum of much
heralded successes of face processing, analysis and recognition on the tasks of
biometric-based identification. We present a case study of automated
statistical inference on sociopsychological perceptions of female faces
controlled for race, attractiveness, age and nationality. Our empirical
evidences point to the possibility of training machine learning algorithms,
using example face images characterized by internet users, to predict
perceptions of personality traits and demeanors.
