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@article{EV-Struc_2009,

title = {Using regression techniques for coping with the one-sample-size problem of face recognition},

author = {Vitomir Štruc and Rok Gajšek and France Mihelič and Nikola Pavešić},

url = {https://lmi.fe.uni-lj.si/en/usingregressiontechniquesforcopingwiththeone-sample-sizeproblemoffacerecognition/},

year  = {2009},

date = {2009-01-01},

urldate = {2009-01-01},

journal = {Electrotechnical Review},

volume = {76},

number = {1-2},

pages = {7-12},

abstract = {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 for 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 modifications 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 is a comparative assessment of several regression techniques and some popular face 

recognition methods.},

keywords = {biometrics, face recognition, one sample size problem, regression techniques, small sample size},

pubstate = {published},

tppubtype = {article}

}