Coarse-to-Fine Iris Recognition Based on Multi-variant Ordinal Measures Feature Complementarity

Hui Zhang, Man Zhang, Zhaofeng He, Hang Zou, Rui Wang

Abstract: Iris recognition inevitably need to tackle extremely large scale database matching issue which challenges the iris recognition in both computing efficiency and accuracy. As a feasible solution, the iris image classification has great potential and needs further studies. We propose a multi-variant Ordinal Measures feature complementarity based coarse-to-fine iris recognition strategy. Two OM variant feature are proposed for iris classification. One is very large scale OM feature (VLSOM), and the other is histogram statistics of OM Run-Length Coding (HOMRLC). VLSOM, HOMRLC and OM describes overall appearance, global statistic and local characteristics of iris respectively. Extensive experiments show advantages of the proposed complementarity feature.