PCA for gender estimation: Which eigenvectors contribute?

Balci K., Atalay V.

16th International Conference on Pattern Recognition (ICPR), QUEBEC CITY, Kanada, 11 - 15 Ağustos 2002, ss.363-366 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: QUEBEC CITY
  • Basıldığı Ülke: Kanada
  • Sayfa Sayıları: ss.363-366
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.