  The massive availability of cameras results in a wide variability of imaging
conditions, producing large intra-class variations and a significant
performance drop if heterogeneous images are compared for person recognition.
However, as biometrics is deployed, it is common to replace damaged or obsolete
hardware, or to exchange information between heterogeneous applications.
Variations in spectral bands can also occur. For example, surveillance face
images (typically acquired in the visible spectrum, VIS) may need to be
compared against a legacy iris database (typically acquired in near-infrared,
NIR). Here, we propose a multialgorithmic approach to cope with periocular
images from different sensors. With face masks in the front line against
COVID-19, periocular recognition is regaining popularity since it is the only
face region that remains visible. We integrate different comparators with a
fusion scheme based on linear logistic regression, in which scores are
represented by log-likelihood ratios. This allows easy interpretation of scores
and the use of Bayes thresholds for optimal decision-making since scores from
different comparators are in the same probabilistic range. We evaluate our
approach in the context of the Cross-Eyed Competition, whose aim was to compare
recognition approaches when NIR and VIS periocular images are matched. Our
approach achieves EER=0.2% and FRR of just 0.47% at FAR=0.01%, representing the
best overall approach of the competition. Experiments are also reported with a
database of VIS images from different smartphones. We also discuss the impact
of template size and computation times, with the most computationally heavy
comparator playing an important role in the results. Lastly, the proposed
method is shown to outperform other popular fusion approaches, such as the
average of scores, SVMs or Random Forest.
