Abstract: An apparatus for and method of performing a most informative feature extraction (MIFE) method in which a facial image is separated into sub-regions, and each sub-region makes individual contribution for performing facial recognition. Specifically, each sub-region is subjected to a sub-region based adaptive gamma (SadaGamma) correction or sub-region based histogram equalization (SHE) in order to account for different illuminations and expressions. A set of reference images is also divided into sub-regions and subjected to the SadaGamma correction or SHE. A comparison is made between the each corrected sub-region and each corresponding sub-region of the reference images. Based upon the comparisons made individually for the sub-regions of the facial image, one of the stored reference images having the greatest correspondence is chosen.

Abstract: A global localization apparatus, medium, and method, with the global localization method including selecting one from a plurality of samples and shifting the selected sample according to a movement of a robot, generating a new sample within a predetermined range of the shifted sample, determining either the shifted sample or the new sample as a next sample at a next time step according to a predetermined condition for the shifted sample and the next sample, repeating for all the samples, and estimating a next position of the robot according to positions of the next samples when the number of next samples is equal to or larger than the maximum number of samples.