Image processing
This is an incremental improvement for face recognition systems, building on existing Gabor filter techniques.
The paper tackled face recognition by proposing Gabor Surface Feature (GSF), a method that fully describes the shape of Gabor magnitude pictures using magnitude, derivatives, binarization, and histograms, achieving effectiveness on FERET, ORL, and FRGC 1.0.4 databases.
Gabor filters can extract multi-orientation and multiscale features from face images. Researchers have designed different ways to use the magnitude of the filtered results for face recognition: Gabor Fisher classifier exploited only the magnitude information of Gabor magnitude pictures (GMPs); Local Gabor Binary Pattern uses only the gradient information. In this paper, we regard GMPs as smooth surfaces. By completely describing the shape of GMPs, we get a face representation method called Gabor Surface Feature (GSF). First, we compute the magnitude, 1st and 2nd derivatives of GMPs, then binarize them and transform them into decimal values. Finally we construct joint histograms and use subspace methods for classification. Experiments on FERET, ORL and FRGC 1.0.4 database show the effectiveness of GSF.