Model Based Selection and Classification of Local Features for Recognition Using Gabor Filters

We propose models based on Gabor functions to address two related aspects in the object recognition problem: interest point selection and classification. We formulate the interest point selection problem by a cascade of bottom-up and top-down stages. We define a novel type of top-down saliency operator to incorporate low-level object related knowledge very soon in the recognition process, thus reducing the number of canditates. For the classification process, we represent each interest point by a vector of Gabor responses whose parameters are automatically selected. Both the selection and classification procedures are designed to be invariant to rotations and scaling. We apply the approach to the problem of facial landmark classification and present experimental result illustrating the performance of the proposed techniques.