In this paper, we introduce a novel Gabor based Spacial Domain Class-Dependence Feature Analysis(GSD-CFA)
method that increases the Face Recognition Grand Challenge (FRGC)2.0 performance. In short, we integrate
Gabor image representation and spacial domain Class-Dependence Feature Analysis(CFA) method to perform
fast and robust face recognition. In this paper, we mainly concentrate on the performances of subspace-based
methods using Gabor feature. As all the experiments in this study is based on large scale face recognition
problems, such as FRGC, we do not compare the algorithms addressing small sample number problem. We study
the generalization ability of GSD-CFA on THFaceID data set. As FRGC2.0 Experiment #4 is a benchmark test
for face recognition algorithms, we compare the performance of GSD-CFA with other famous subspace-based
algorithms in this test.
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