DocumentCode :
2455213
Title :
PCA for gender estimation: which eigenvectors contribute?
Author :
Balci, Koray ; Atalay, Volkan
Author_Institution :
LORIA, Vandoeuvre-les-Nancy, France
Volume :
3
fYear :
2002
fDate :
2002
Firstpage :
363
Abstract :
A pruning schema is applied to multi-layer perceptron (MLP) gender classifier MLP uses eigenvector coefficients 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.
Keywords :
eigenvalues and eigenfunctions; face recognition; multilayer perceptrons; principal component analysis; eigenvector coefficients; gender estimation; multi-layer perceptron gender classifier; principal component analysis; pruning schema; Degradation; Face recognition; Humans; Image analysis; Image databases; Image resolution; Multilayer perceptrons; Principal component analysis; Robustness; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2002. Proceedings. 16th International Conference on
ISSN :
1051-4651
Print_ISBN :
0-7695-1695-X
Type :
conf
DOI :
10.1109/ICPR.2002.1047869
Filename :
1047869
Link To Document :
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