  Periocular recognition has gained attention recently due to demands of
increased robustness of face or iris in less controlled scenarios. We present a
new system for eye detection based on complex symmetry filters, which has the
advantage of not needing training. Also, separability of the filters allows
faster detection via one-dimensional convolutions. This system is used as input
to a periocular algorithm based on retinotopic sampling grids and Gabor
spectrum decomposition. The evaluation framework is composed of six databases
acquired both with near-infrared and visible sensors. The experimental setup is
complemented with four iris matchers, used for fusion experiments. The eye
detection system presented shows very high accuracy with near-infrared data,
and a reasonable good accuracy with one visible database. Regarding the
periocular system, it exhibits great robustness to small errors in locating the
eye centre, as well as to scale changes of the input image. The density of the
sampling grid can also be reduced without sacrificing accuracy. Lastly, despite
the poorer performance of the iris matchers with visible data, fusion with the
periocular system can provide an improvement of more than 20%. The six
databases used have been manually annotated, with the annotation made publicly
available.
