One of the major challenges in ocular biometrics is the cross-spectral scenario, i.e. how to match images acquired in different wavelengths. This study designs and extensively evaluates cross-spectral ocular verification methods using well known deep learning representations based on the iris and periocular regions. Using as inputs, the bounding boxes of non-normalised iris-periocular regions, the authors fine-tune convolutional neural network models, originally trained for face recognition. On the basis of the experiments carried out in two publicly available cross-spectral ocular databases, they report results for intra-spectral and cross-spectral scenarios, with the best performance being observed when fusing ResNet-50 deep representations from both the periocular and iris regions. When compared to the state of the art, they observed that the proposed solution consistently reduces the equal error rate values by 90%/93%/96% and 61%/77%/83% on the cross-spectral scenario and in the PolyU bi-spectral and cross-eye-cross-spectral datasets. Finally, they evaluate the effect that the `deepness' factor of feature representations has in recognition effectiveness, and based on a subjective analysis of the most problematic pairwise comparisons - they point out further directions for this field of research.

中文翻译：

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跨光谱眼图生物特征识别的深层表示

眼部生物识别技术的主要挑战之一是跨光谱方案，即如何匹配以不同波长采集的图像。本研究使用众所周知的基于虹膜和眼周区域的深度学习表示，设计并广泛评估了跨光谱眼图验证方法。使用非标准化虹膜-眼周区域的边界框作为输入，作者微调了最初训练用于人脸识别的卷积神经网络模型。根据在两个可公开使用的互谱眼图数据库中进行的实验，他们报告了谱内和互谱场景的结果，在融合ResNet-50时观察到最佳性能来自眼周和虹膜区域的深层表现。当与现有技术进行比较时，他们观察到所提出的解决方案在跨光谱场景和PolyU中始终将相等错误率值降低了90％/ 93％/ 96％和61％/ 77％/ 83％双光谱和双眼交叉光谱数据集。最后，他们评估了特征表示的“深度”因素对识别效果的影响，并基于对最成问题的成对比较的主观分析-他们指出了该研究领域的进一步方向。