Large Scale Natural Vision
نویسندگان
چکیده
A computationally intensive approach to pattern recognition in images is developed and applied to face recognition. Similarly to previous work, we compute functional inner products of a two-dimensional input signal (image) with a set of two-dimensional Gabor functions which t the receptive elds of simple cells in the primary visual cortex of mammals. The proposed model includes non-linearities, such as thresholding, orientation competition and lateral inhibition. The output of the model is a set of cortical images each of which contains only edge lines of a particular orientation in a particular light-to-dark transition direction. In this way the information of the original image is split into diierent channels. The cortical images are used to compute a lower-dimension space representation for object recognition. The method was implemented on the Connection Machine CM-5 1 and achieved a recognition rate of 97% when applied to a large database of face images.
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