A machine learning predictor of facial attractiveness revealing human-like psychophysical biases.
Vision Res
; 48(2): 235-43, 2008 Jan.
Article
em En
| MEDLINE
| ID: mdl-18164363
ABSTRACT
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Reconhecimento Visual de Modelos
/
Beleza
/
Inteligência Artificial
/
Face
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Female
/
Humans
Idioma:
En
Ano de publicação:
2008
Tipo de documento:
Article