Log-Likelihood Score Level Fusion for Improved Cross-Sensor Smartphone
  Periocular Recognition
Fernando Alonso-Fernandez Kiran B. Raja Christoph Busch Josef Bigun
The proliferation of cameras and personal devices results in a wide
variability of imaging conditions, producing large intra-class variations and a
significant performance drop when images from heterogeneous environments are
compared. However, many applications require to deal with data from different
sources regularly, thus needing to overcome these interoperability problems.
Here, we employ fusion of several comparators to improve periocular performance
when images from different smartphones are compared. We use a probabilistic
fusion framework based on linear logistic regression, in which fused scores
tend to be log-likelihood ratios, obtaining a reduction in cross-sensor EER of
up to 40% due to the fusion. Our framework also provides an elegant and simple
solution to handle signals from different devices, since same-sensor and
cross-sensor score distributions are aligned and mapped to a common
probabilistic domain. This allows the use of Bayes thresholds for optimal
decision-making, eliminating the need of sensor-specific thresholds, which is
essential in operational conditions because the threshold setting critically
determines the accuracy of the authentication process in many applications.