虹膜识别技术是基于眼睛中的虹膜进行身份识别,应用于安防设备(如门禁等),以及有高度保密需求的场所。 人的眼睛结构由巩膜、虹膜、瞳孔晶状体、视网膜等部分组成。虹膜是位于黑色瞳孔和白色巩膜之间的圆环状部分,其包含有很多相互交错的斑点、细丝、冠状、条纹、隐窝等的细节特征。而且虹膜在胎儿发育阶段形成后,在整个生命历程中将是保持不变的。这些特征决定了虹膜特征的唯一性,同时也决定了身份识别的唯一性。因此,可以将眼睛的虹膜特征作为每个人的身份识别对象。 例如,在好莱坞大片中,通过扫描眼睛视网膜开启保密房间或保险箱的炫酷场景,大家一定还记忆犹新吧!使用虹膜识别技术,为需要高度保密的场所提供了高度安全保障。

最新论文

In recent years, cross-spectral iris recognition has emerged as a promising biometric approach to establish the identity of individuals. However, matching iris images acquired at different spectral bands (i.e., matching a visible (VIS) iris probe to a gallery of near-infrared (NIR) iris images or vice versa) shows a significant performance degradation when compared to intraband NIR matching. Hence, in this paper, we have investigated a range of deep convolutional generative adversarial network (DCGAN) architectures to further improve the accuracy of cross-spectral iris recognition methods. Moreover, unlike the existing works in the literature, we introduce a resolution difference into the classical cross-spectral matching problem domain. We have developed two different techniques using the conditional generative adversarial network (cGAN) as a backbone architecture for cross-spectral iris matching. In the first approach, we simultaneously address the cross-resolution and cross-spectral matching problem by training a cGAN that jointly translates cross-resolution as well as cross-spectral tasks to the same resolution and within the same spectrum. In the second approach, we design a coupled generative adversarial network (cpGAN) architecture consisting of a pair of cGAN modules that project the VIS and NIR iris images into a low-dimensional embedding domain to ensure maximum pairwise similarity between the feature vectors from the two iris modalities of the same subject.

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