@inproceedings{8bafe2b934f5451cb8137d136b9d6a23,
title = "Learning structurally discriminant features in 3D faces",
abstract = "In this paper, we derive a data mining framework to analyze 3D features on human faces. The framework leverages kernel density estimators, genetic algorithm and an information complexity criterion to identify discriminant feature-clusters of lower dimensionality. We apply this framework on human face anthropometry data of 32 features collected from each of the 300 3D face mesh models. The feature-subsets that we infer as the output establishes domain knowledge for the challenging problem of 3D face recognition with dense 3D gallery models and sparse or low resolution probes.",
keywords = "3D face recognition, Dimensionality reduction, Feature learning, Informative-discrimant face features",
author = "Sukumar, \{Sreenivas R.\} and Hamparsum Bozdogan and Page, \{David L.\} and Koschan, \{Andreas F.\} and Abidi, \{Mongi A.\}",
year = "2008",
doi = "10.1109/ICIP.2008.4712154",
language = "English",
isbn = "1424417643",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "1912--1915",
booktitle = "2008 IEEE International Conference on Image Processing, ICIP 2008 Proceedings",
note = "2008 IEEE International Conference on Image Processing, ICIP 2008 ; Conference date: 12-10-2008 Through 15-10-2008",
}