Iris recognition using ordinal encoding of Log-Euclidean covariance matrices

Iris recognition in less constrained environments is challenging due to the degraded iris images. This paper proposes a novel method fusing multiple cues for iris recognition in the non-ideal imagery. The covariance matrices are used to represent local iris texture property, which capture the correlation of spatial coordinates, intensities, 1st and 2nd-order partial derivatives. The covariance matrices are symmetric positive definite (SPD) which form a Riemannian space rather than a Euclidean one. In the Log-Euclidean framework, the space of SPD matrices is equipped with a linear space structure so that in the logarithmic domain the Euclidean operations are applicable. This enables us to compute the logarithms of covariance matrices, leading to the Log-Euclidean covariance matrices (LECM), which can be handled in common Euclidean operations. The ordinal measure is further used to represent the order relation of iris texture by comparing LECMs at different positions. We finally perform iris matching based on the Hamming distance in which the noise effects are considered. Experiments on challenging databases show the effectiveness of the proposed method.