Abstract: An object identification system iteratively learns both a template map used to transform a template describing an object in an image, and a related similarity metric used in comparing one transformed object template to another. This automatic learning eliminates the need to manually devise a transformation and metric that are effective for a given image corpus. The template map and the similarity metric are learned together, such that the incremental component to be added to the template map at a given iteration of the learning process is based at least in part on the components of the similarity metric, and vice-versa.

Abstract: This disclosure describes methods to integrate face, skin and iris recognition to provide a biometric system for identifying individuals. These methods only require a digital image depicting a human face as source data.

Abstract: The present invention may be embodied in a method, and in a related apparatus, for classifying a feature in an image frame. In the method, an original image frame having an array of pixels is transformed using Gabor-wavelet transformations to generate a transformed image frame. Each pixel of the transformed image is associated with a respective pixel of the original image frame and is represented by a predetermined number of wavelet component values. A pixel of the transformed image frame associated with the feature is selected for analysis. A neural network is provided that has an output and a predetermined number of inputs. Each input of the neural network is associated with a respective wavelet component value of the selected pixel. The neural network classifies the local feature based on the wavelet component values, and indicates a class of the feature at an output of the neural network.