Automated Inference on Sociopsychological Impressions of Attractive Female Faces
This work addresses automated social computing for face perception, but it is incremental as it builds on prior research and focuses on a specific case study.
The authors tackled the problem of predicting sociopsychological impressions from female faces using supervised machine learning, finding that algorithms can be trained on internet-characterized images to infer personality traits and demeanors.
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.