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 R-SAN net is suitable for orientation recognition of a concentric circular pattern such as an iris image. Previously, variations of ambient lighting conditions affected iris detection. In this study, we introduce brightness normalization for accuracy improvement of iris detection in various lighting conditions. Brightness normalization provides high accuracy iris extraction in severe lighting conditions. A recognition experiment investigated the characteristics of rotation and shape recognition for both learned and un-learned iris images in various plane rotations. The R-SAN net recognized the rotation angle for the learned iris images in arbitrary orientation, but not for un-learned iris images. Thus, the variation of the rotation angle was corrected only for learned irises, but not un-learned irises. Although the R-SAN net rightly recognized the learned irises, it could not completely reject the un-learned irises as unregistered irises. Using the specific orientation recognition characteristics of the R-SAN net, a minimum distance was introduced as a new shape recognition criterion for the R-SAN net. In consequence, the R-SAN net combined with the minimum distance rightly recognized both learned (registered) and un-learned irises; the unregistered irises were correctly rejected.

Although previous studies using artificial neural networks have been actively applied to object shape recognition, little attention has been paid to the recognition of spatial elements (e.g. position, rotation and size). In the present study, a rotation and size spreading associative neural network (RS-SAN net) is proposed and the efficacy of the RS-SAN net in object orientation (rotation), size and shape recognition is shown. The RS-SAN net pays attention to the fact that the spatial recognition system in the brain (parietal cortex) is involved in both the spatial (e.g. position, rotation and size) and shape recognition of an object. The RS-SAN net uses spatial spreading by spreading layers, generalized inverse learning and population vector methods for the recognition of the object. The information of the object orientation and size is spread by double spreading layers which have similar tuning characteristics to spatial discrimination neurons (e.g. axis orientation neurons and size discrimination neurons) in the parietal cortex. The RS-SAN net simultaneously recognizes the size of the object irrespective of its orientation and shape, the orientation irrespective of its size and shape, and the shape irrespective of its size and orientation.