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Activity Number: 630 - Machine Learning Applications
Type: Contributed
Date/Time: Thursday, August 3, 2017 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #323406
Title: Statistial Learning in Gender Classification for Facial Images
Author(s): Cuixian Chen* and Yishi Wang
Companies: University of North Carolina, Wilmington and University of North Carolina Wilmington
Keywords: Gender classification ; dimension reduction ; variable selection ; human perception
Abstract:

Many believe that gender classification is a solved problem, however, gender classification for children is a very difficult problem that has not been adequately addressed by the research community. In this work we demonstrate this fact and present a system that performs gender classification on children that outperforms humans. Motivated by the significant improvement in model selection for age estimation, we investigate a robust gender classification system via model selection and evaluate the systems using leave-one-person-out cross-validation and 5-fold cross-validation schemes on FG-NET database. Furthermore, this work develops a novel operator, graph gender preserving, to build a neighborhood graph for locality preserving projection for gender classification.


Authors who are presenting talks have a * after their name.

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