C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Making large-scale support vector machine learning practical
Advances in kernel methods
Kernel principal component analysis
Advances in kernel methods
Machine Learning
Machine Learning
Facial beauty and fractal geometry
Facial beauty and fractal geometry
ACM SIGGRAPH 2006 Sketches
Data-driven enhancement of facial attractiveness
ACM SIGGRAPH 2008 papers
Social signal processing: state-of-the-art and future perspectives of an emerging domain
MM '08 Proceedings of the 16th ACM international conference on Multimedia
A bi-prototype theory of facial attractiveness
Neural Computation
Interactive Cosmetic Makeup of a 3D Point-Based Face Model
IEICE - Transactions on Information and Systems
Social signal processing: Survey of an emerging domain
Image and Vision Computing
A virtual environment for 3D facial makeup
ICVR'07 Proceedings of the 2nd international conference on Virtual reality
Quantitative analysis of human facial beauty using geometric features
Pattern Recognition
Semantic analysis and retrieval in personal and social photo collections
Multimedia Tools and Applications
A survey of perception and computation of human beauty
J-HGBU '11 Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding
A benchmark for geometric facial beauty study
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
The analysis of facial beauty: an emerging area of research in pattern analysis
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Sense beauty via face, dressing, and/or voice
Proceedings of the 20th ACM international conference on Multimedia
Towards decrypting attractiveness via multi-modality cues
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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This work presents a novel study of the notion of facial attractiveness in a machine learning context. To this end, we collected human beauty ratings for data sets of facial images and used various techniques for learning the attractiveness of a face. The trained predictor achieves a significant correlation of 0.65 with the average human ratings. The results clearly show that facial beauty is a universal concept that a machine can learn. Analysis of the accuracy of the beauty prediction machine as a function of the size of the training data indicates that a machine producing human-like attractiveness rating could be obtained given a moderately larger data set.