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Learning structurally discriminant features in 3D faces
2008
2008 15th IEEE International Conference on Image Processing
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
doi:10.1109/icip.2008.4712154
dblp:conf/icip/SukumarBPKA08
fatcat:jraejddabrcpvnt424lq2vfgui