A machine learning predictor of facial attractiveness revealing human-like psychophysical biases.
Recent psychological studies have strongly suggested that humans share common visual preferences for facial attractiveness. Here, we present a learning model that automatically extracts measurements of facial features from raw images and obtains human-level performance in predicting facial attractiveness ratings. The machine's ratings are highly correlated with mean human ratings, markedly improving on recent machine learning studies of this task. Simulated psychophysical experiments with virtually manipulated images reveal preferences in the machine's judgments that are remarkably similar to those of humans. Thus, a model trained explicitly to capture a specific operational performance criteria, implicitly captures basic human psychophysical characteristics.
Kagian
Amit
A
School of Computer Sciences, Tel-Aviv University, Tel-Aviv 69978, Israel. amit.kagian@gmail.com
Dror
Gideon
G
Leyvand
Tommer
T
Meilijson
Isaac
I
Cohen-Or
Daniel
D
Ruppin
Eytan
E
D000465:Algorithms; D001185:Artificial Intelligence; D001504:Beauty; D005145:Face / Q000033:anatomy & histology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D007600:Judgment; D010364:Pattern Recognition, Visual; D010781:Photography; D011601:Psychophysics; D015203:Reproducibility of Results