Single training sample Face recognition using fusion of Gabor responses

This paper deals with Single training sample Face recognition which is a new challenging problem in pattern recognition and machine vision. In the proposed model, Gabor filter is applied on face images, to generate four Gabor responses. And then, the generated samples along with the original image used as the input of five Nearest Neighbor Base-classifiers. To increase the correct recognition rate, the votes of Base-Classifiers are combined using majority voting technique. The correct class of each test sample is determined by the majority of the classifiers votes. But in cases which the winner class can't be determined using majority voting (deep uncertainty), we use Enhanced Majority voting which chooses the vote of original image's Base-Classifier as the winner class. The resultant recognition rate of applying this model on ORL face database was improvement in recognition rate about 2%, 4% and 5% than 2DPCA, (PC)
²
A and PCA respectively.