Iris recognition using quaternionic sparse orientation code (QSOC)

Personal identification from the iris images acquired under less-constrained imaging environment is highly challenging problem but with several important applications in surveillance, image forensics, search for missing children and wandering elderly. In this paper, we develop and formulate a new approach for the iris recognition using hypercomplex (quaternionic or octonionic) and sparse representation of unwrapped iris images. We model iris representation problem as quaternionic sparse coding problem which is solved by convex optimization strategy. This approach essentially exploits the orientation of local iris texture elements which are efficiently extracted using a binarized dictionary of oriented atoms. The feasibility of this approach is evaluated, both for the recognition and the verification problem, on the publicly available visible illumination UBIRIS V2 database. Our experimental results using the proposed formulation illustrate significant improvement in performance (e.g., ~30% improvement in rank-one recognition accuracy) over the previously studied sparse representation approach for the visible illumination iris recognition.