论文标题
跨光谱眼匹配的频谱翻译
Spectrum Translation for Cross-Spectral Ocular Matching
论文作者
论文摘要
跨光谱验证仍然是生物识别技术的一个大问题，特别是对于眼部区域，由于图像中反映特征的差异取决于所使用的区域和光谱。 在本文中，我们研究了有条件的对抗网络在眼部生物识别技术的近红外图像和视觉光图像之间的频谱翻译。我们根据转换图像的整体视觉质量分析转换，并在接受相反数据训练时识别系统的准确性下降。 我们使用Polyu数据库，并提出了两个不同的系统进行生物识别验证，这是第一个基于暹罗网络，该网络训练有软磁性和跨透镜丢失，第二个是一个是三胞胎损失网络。当使用对NIR训练的三胞胎损失网络并在真实的NIR图像和从可见光频谱中翻译的伪造图像之间找到欧几里得距离时，我们达到了1 \％的EER。我们还使用基线算法优于先前的结果。
Cross-spectral verification remains a big issue in biometrics, especially for the ocular area due to differences in the reflected features in the images depending on the region and spectrum used. In this paper, we investigate the use of Conditional Adversarial Networks for spectrum translation between near infra-red and visual light images for ocular biometrics. We analyze the transformation based on the overall visual quality of the transformed images and the accuracy drop of the identification system when trained with opposing data. We use the PolyU database and propose two different systems for biometric verification, the first one based on Siamese Networks trained with Softmax and Cross-Entropy loss, and the second one a Triplet Loss network. We achieved an EER of 1\% when using a Triplet Loss network trained for NIR and finding the Euclidean distance between the real NIR images and the fake ones translated from the visible spectrum. We also outperform previous results using baseline algorithms.
