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.

翻译：摄像头与个人设备的普及导致成像条件存在广泛差异，产生较大的类内变化，当来自异质环境的图像进行比较时，性能显著下降。然而，许多应用需要频繁处理来自不同来源的数据，因此必须克服这些互操作性问题。本文采用多种比较器的融合方法，以提升不同智能手机图像间的眼周识别性能。我们使用基于线性逻辑回归的概率融合框架，其中融合分数趋向于对数似然比，通过融合使跨传感器的等错误率（EER）降低高达40%。该框架还提供了一种优雅且简单的解决方案来处理来自不同设备的信号，因为同传感器和跨传感器的分数分布被对齐并映射到公共概率域。这使得能够使用贝叶斯阈值进行最优决策，无需针对特定传感器设置阈值，这在操作条件下至关重要，因为阈值设定在许多应用中关键决定了认证过程的准确性。