Iris Recognition Independent of Rotation and Ambient Lighting Variations

We previously proposed a rotation spreading neural network (R-SAN net). This neural net can recognize the orientation of an object irrespective of its shape, and its shape irrespective of its orientation. The characteristics of the R-SAN net are to spread information about the axis orientation of the object in relation to surrounding space by spreading weight with similar tuning characteristics to axis orientation neurons in the parietal cortex. The R-SAN net is suitable for orientation recognition of a concentric circular pattern such as iris image, because it uses polar conversion. Previously, variations of ambient lighting conditions affected iris detection. In this study, we introduced brightness normalization for accuracy improvement of iris detection in various lighting conditions. Brightness normalization provided high accuracy iris extraction in severe lighting conditions. We also attempted real-time individual identification with the iris patterns. In the recognition experiment, the R-SAN net combined with the minimum distance was able to accurately recognize the orientation and shape of iris images in arbitrary orientation. We suggest application of the R-SAN net as an iris recognition system.