Automated Inference on Sociopsychological Impressions of Attractive Female Faces

This article is a sequel to our earlier work [24]. 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. Like in [24], our empirical evidences point to the possibility of training machine learning algorithms, using example face images, to predict personality traits and behavioral propensity.