New colour SIFT descriptors for image classification with applications to biometrics

This paper first presents a new oRGB-SIFT descriptor, and then integrates it with other color SIFT features to produce the novel Color SIFT Fusion (CSF) and the Color Grayscale SIFT Fusion (CGSF) descriptors for image classification with special applications to biometrics. Classification is implemented using a novel EFM-KNN classifier, which combines the Enhanced Fisher Model (EFM) and the K Nearest Neighbor (KNN) decision rule. The effectiveness of the proposed descriptors and classification method are evaluated using 20 image categories from two large scale, grand challenge datasets: the Caltech 256 database and the UPOL Iris database. The experimental results show that (i) the proposed oRGB-SIFT descriptor slightly improves recognition performance upon other color SIFT descriptors; and (ii) both the CSF and the CGSF descriptors perform better than the other color SIFT descriptors.