PCA for gender estimation: Which eigenvectors contribute?


Balci K., Atalay V.

16th International Conference on Pattern Recognition (ICPR), QUEBEC CITY, Canada, 11 - 15 August 2002, pp.363-366 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • City: QUEBEC CITY
  • Country: Canada
  • Page Numbers: pp.363-366

Abstract

A pruning schema is applied to Multi-Layer Perceptron (MLP) gender classifer MLP uses eigenvector coeffcients of the face space created by Principal Component Analysis (PCA). We show that pruning improves the initial MLP performance by preserving the most effective input while eliminating most of the units and connections. Pruning is also used as a tool to monitor which eigenvectors contribute to gender estimation. In addition, by usage of FERET face database, we test the PCA approach on gender estimation task in a bigger setting than the previous experiments.