Zernike moment invariants based photo-identification using Fisher discriminant model

This paper presents a photo-identification algorithm using Zernike moment invariants embedded in a subspace optimal for pattern identification. Fisher discriminants are used and the invariants are projected onto the subspace spanned by the Fisher basis vectors. The technique has been applied to photo-identification of gray whales (Eschrichtius robustus) using their field images. White patches (blotches) appearing on a gray whale's left and right flukes constitute unique identifying features and have been used here for individual identification. The fluke area is extracted from a fluke image via the live-wire edge detection algorithm, followed by optimal thresholding of the fluke area to obtain the blotches. Zernike moment invariants are then calculated for the blotches and projected onto the subspace spanned by Fisher basis vectors. These invariants are used as the feature vector representing a database image. During matching, the database images are ranked depending on the degree of similarity between a query and database feature vectors. The results show that the use of this algorithm leads to a significant reduction in the amount of manual search that is normally done by marine biologists.