Individual discriminative face recognition models based on subsets of features |
| | Abstract | The accuracy of data classification methods depends considerably
on the data representation and on the selected features. In this
work, the elastic net model selection is used to identify
meaningful and important features in face recognition. Modelling
the characteristics which distinguish one person from another
using only subsets of features will both decrease the
computational cost and increase the generalization capacity of the
face recognition algorithm. Moreover, identifying which are the
features that better discriminate between persons will also
provide a deeper understanding of the face recognition problem.
The elastic net model is able to select a subset of features with
low computational effort compared to other state-of-the-art
feature selection methods. Furthermore, the fact that the number
of features usually is larger than the number of images in the
data base makes feature selection techniques such as forward
selection or lasso regression become inadequate. In the
experimental section, the performance of the elastic net model is
compared with geometrical and color based algorithms widely used
in face recognition such as Procrustes nearest neighbor,
Eigenfaces, or Fisherfaces. Results show that the elastic net is
capable of selecting a set of discriminative features and hereby
obtain high classification rates. | | Type | Conference paper [With referee] | | Conference | SCIA 2007, LNCS 4522 proceedings | | Year | 2007 pp. 61-71 | | Electronic version(s) | [pdf] | | BibTeX data | [bibtex] | | IMM Group(s) | Image Analysis & Computer Graphics |
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