Gabor Parameter Selection for Local Feature Detection

Some recent works have addressed the object recognition problem by representing objects as the composition of independent image parts, where each part is modeled with “low-level” features. One of the problems to address is the choice of the low-level features to appropriately describe the individual image parts. Several feature types have been proposed, like edges, corners, ridges, Gaussian derivatives, Gabor features, etc. Often features are selected independently of the object to represent and have fixed parameters. In this work we use Gabor features and describe a method to select feature parameters suited to the particular object considered. We propose a method based on the Information Diagram concept, where “good” parameters are the ones that optimize the filter’s response in the filter parameter space. We propose and compare some concrete methodologies to choose the Gabor feature parameters, and illustrate the performance of the method in the detection of facial parts like eyes, noses and mouths. We show also the rotation invariance and robustness to small scale changes of the proposed Gabor feature.