This work is part of a retrospective collection of 179 electronic theses and dissertations (ETDs) from the VCU Libraries pilot ETD system that were designated as available only to VCU users. Please contact us at if you have questions or if you are the author of one of these and would like to release it for online public access.
Non-VCU users: Please talk to your librarian about requesting this thesis through interlibrary loan.
Defense Date
2005
Document Type
Thesis
Degree Name
Master of Science
Department
Computer Science
Abstract
Artificial Neural Networks (ANNs) are powerful predictors, however, they essentially function like 'black boxes' because they lack explanatory power. Various algorithms have been developed to examine input influences and interactions thus enhancing understanding of the function being modeled. The study of facial attractiveness is one domain that could potentially benefit from ANN models. The literature shows that the relationship between attractiveness and facial attributes is complex and not yet fully understood. In this project, a feed-forward ANN was trained with backpropagation to 0.86 classification using 8-fold cross validation. The dataset consisted of 88 female facial images, each containing 17 geofacial measurements, a random noise variable, and a rating. Input 'clamping' and the Connection Weight Approach (Olden & Jackson, 2002), were implemented and the results were examined in terms of the facial attractiveness domain. In general, the results suggest that more feminized and asymmetrical features enhance facial attractiveness.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
June 2008
VCU Only:
Off Campus Download
Comments
Part of Retrospective ETD Collection, restricted to VCU only.