Using Regression Techniques for Coping with the One-Sample-Size Problem of Face Recognition
Rok Gaj · 2009
There is a number of face recognition paradigms which ensure good recognition rates with frontal face images. However, the majority of them require an extensive training set and degrade in their performance when an insufficient number of training images is available. This is especially true fo r applications where only one image per subject is at hand for training. To cope with this one-sample-size(OSS) problem, we propose to employ subspace projection based regression techniques rather than modifica tions of the established face recognition paradigms, such as the principal component or linear discriminant analysis, as it was done in the past. Experiments performed on the XM2VTS and ORL databases show the effectiveness of the proposed approach. Also presented ia a comparative assessment of several regression techniques and some popular face recognition methods.