Publications:Log-Likelihood Score Level Fusion for Improved Cross-Sensor Smartphone Periocular Recognition


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Title Log-Likelihood Score Level Fusion for Improved Cross-Sensor Smartphone Periocular Recognition
Author Fernando Alonso-Fernandez and Kiran B. Raja and Christoph Busch and Josef Bigun
Year 2017
PublicationType Conference Paper
HostPublication 2017 25th European Signal Processing Conference (EUSIPCO)
Conference 2017 25th European Signal Processing Conference (EUSIPCO 2017), 28 Aug.-2 Sept., 2017, Kos Island, Greece
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Abstract 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 crosssensor 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. © EURASIP 2017