This article is a sequel to our earlier paper . Our main objective 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 , our empirical evidences point to the possibility of teaching computer vision and machine learning algorithms, using example face images, to predict personality traits and behavioral predisposition.
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