  Ocular biometric systems working in unconstrained environments usually face
the problem of small within-class compactness caused by the multiple factors
that jointly degrade the quality of the obtained data. In this work, we propose
an attribute normalization strategy based on deep learning generative
frameworks, that reduces the variability of the samples used in pairwise
comparisons, without reducing their discriminability. The proposed method can
be seen as a preprocessing step that contributes for data regularization and
improves the recognition accuracy, being fully agnostic to the recognition
strategy used. As proof of concept, we consider the "eyeglasses" and "gaze"
factors, comparing the levels of performance of five different recognition
methods with/without using the proposed normalization strategy. Also, we
introduce a new dataset for unconstrained periocular recognition, composed of
images acquired by mobile devices, particularly suited to perceive the impact
of "wearing eyeglasses" in recognition effectiveness. Our experiments were
performed in two different datasets, and support the usefulness of our
attribute normalization scheme to improve the recognition performance.
