Cross-Spectral Iris Recognition by Learning Device-Specific Band
Cross-Spectral Iris Recognition by Learning Device-Specific Band
复制标题
通过学习设备特定频段进行跨光谱虹膜识别
DOI：
10.1109/tcsvt.2021.3117291
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发表时间：
2022-06
影响因子：
8.4
通讯作者：
Zhenan Sun
中科院分区：
文献类型：
--
作者：
Jianze Wei;Yunlong Wang;Yi Li;Ran He;Zhenan Sun
互谱识别是虹膜识别领域的一个重要课题。在跨光谱虹膜识别中，近红外（NIR）和可见光（维斯）图像之间存在明显的设备特征波段，导致不同光谱样本之间的分布差异，从而严重影响识别性能。为了解决这个问题，我们提出了一种新的跨光谱虹膜识别方法，通过估计设备特定的波段来学习光谱不变的特征。在该方法中，Gabor三叉神经网络（GTN）首先利用Gabor函数的先验知识感知不同光谱下的虹膜纹理，然后将设备特定波段编码为残差分量，辅助生成光谱不变特征。通过研究设备特定频段，GTN有效地减少了设备特定频段对身份特征的影响。此外，我们还在三方面努力进一步缩小分配差距。首先，频谱对抗网络（SAN）采用类级对抗策略来对齐特征分布。第二，样本锚（SA）损失升级三重损失拉样本到他们的类中心和推离其他类中心。第三，我们发展了一个高阶排列损失来衡量根据空间基地和分布形状的分布差距。在五个虹膜数据集上的实验证明了该方法在跨光谱虹膜识别中的有效性。
Cross-spectral recognition is still an open challenge in iris recognition. In cross-spectral iris recognition, there exist distinct device-specific bands between near-infrared (NIR) and visible (VIS) images, resulting in the distribution gap between samples from different spectra and thus severe degradation in recognition performance. To tackle this problem, we propose a new cross-spectral iris recognition method to learn spectral-invariant features by estimating device-specific bands. In the proposed method, Gabor Trident Network (GTN) first utilizes the Gabor function’s priors to perceive iris textures under different spectra, and then codes the device-specific band as the residual component to assist the generation of spectral-invariant features. By investigating the device-specific band, GTN effectively reduces the impact of device-specific bands on identity features. Besides, we make three efforts to further reduce the distribution gap. First, Spectral Adversarial Network (SAN) adopts a class-level adversarial strategy to align feature distributions. Second, Sample-Anchor (SA) loss upgrades triplet loss by pulling samples to their class center and pushing away from other class centers. Third, we develop a higher-order alignment loss to measures the distribution gap according to space bases and distribution shapes. Extensive experiments on five iris datasets demonstrate the efficacy of our proposed method for cross-spectral iris recognition.