  In this paper, we present a new approach for periocular recognition based on
the Symmetry Assessment by Feature Expansion (SAFE) descriptor, which encodes
the presence of various symmetric curve families around image key points. We
use the sclera center as single key point for feature extraction, highlighting
the object-like identity properties that concentrates to this unique point of
the eye. As it is demonstrated, such discriminative properties can be encoded
with a reduced set of symmetric curves. Experiments are done with a database of
periocular images captured with a digital camera. We test our system against
reference periocular features, achieving top performance with a considerably
smaller feature vector (given by the use of a single key point). All the
systems tested also show a nearly steady correlation between acquisition
distance and performance, and they are also able to cope well when enrolment
and test images are not captured at the same distance. Fusion experiments among
the available systems are also provided.
