Local matching Gabor entropy weighted face recognition

Face recognition has a wide range of possible applications in surveillance, access control, human computer interfaces and in electronic marketing and advertising for selected customers. Several models based on Gabor feature extraction have been proposed for face recognition with very good results on internationally available face databases. In this paper, we propose a methodological improvement to increase face recognition rate by selection and weighting Gabor jets by an entropy measure. We also propose improvements in the Borda count classification through a threshold to eliminate low score jets from the voting process to increase the face recognition rate. We show that combinations of weighting Gabor jets and threshold Borda yield the best results. We tested our methodological improvements on the FERET and the AR face databases. On the FERET database we reduce the total number of errors from 163 to 102 which is the highest score published up to date. The total number of errors in face recognition was reduced in 37%. On the AR database we also obtained important improvements and tested face images with illumination and gesticulation changes, and occlusions.