Towards More Discriminative and Robust Iris Recognition by Learning Uncertain Factors
Towards More Discriminative and Robust Iris Recognition by Learning Uncertain Factors
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DOI：
10.1109/tifs.2022.3154240
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发表时间：
2022
影响因子：
6.8
通讯作者：
Jianze Wei;Huaibo Huang;Yunlong Wang;R. He;Zhenan Sun
中科院分区：
文献类型：
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作者：
Jianze Wei;Huaibo Huang;Yunlong Wang;R. He;Zhenan Sun
虹膜图像采集过程的不可控性限制了虹膜识别的性能。在获取过程中，各种不可避免的因素，包括眼睛，设备和环境，阻碍虹膜识别系统从学习一个有区别的身份表示。这会导致严重的性能下降。在本文中，我们探讨了不确定的收购因素，并提出不确定性嵌入（UE）和不确定性指导课程学习（UGCL），以减轻收购因素的影响。UE使用概率分布而不是虹膜识别方法中广泛采用的确定性点（二进制模板或特征向量）来表示虹膜图像。具体来说，UE从输入图像中学习身份和不确定性特征，并将它们编码为分布的两个独立分量，即均值和方差。基于这种表示，输入图像可以被视为从UE采样的实例化特征，并且我们还可以通过采样生成各种虚拟特征。UGCL是通过模仿新生儿的渐进学习过程构建的。特别地，它根据虚拟特征的不确定性，在不同的训练阶段以从易到难的顺序选择虚拟特征来训练模型。此外，一个实例级增强方法的开发，利用局部和全局统计，以减轻数据的不确定性，从图像噪声和采集条件的像素级空间。在6个基准虹膜数据集上的实验结果验证了该方法在同传感器和跨传感器识别中的有效性和泛化能力。
The uncontrollable acquisition process limits the performance of iris recognition. In the acquisition process, various inevitable factors, including eyes, devices, and environment, hinder the iris recognition system from learning a discriminative identity representation. This leads to severe performance degradation. In this paper, we explore uncertain acquisition factors and propose uncertainty embedding (UE) and uncertainty-guided curriculum learning (UGCL) to mitigate the influence of acquisition factors. UE represents an iris image using a probabilistic distribution rather than a deterministic point (binary template or feature vector) that is widely adopted in iris recognition methods. Specifically, UE learns identity and uncertainty features from the input image, and encodes them as two independent components of the distribution, mean and variance. Based on this representation, an input image can be regarded as an instantiated feature sampled from the UE, and we can also generate various virtual features through sampling. UGCL is constructed by imitating the progressive learning process of newborns. Particularly, it selects virtual features to train the model in an easy-to-hard order at different training stages according to their uncertainty. In addition, an instance-level enhancement method is developed by utilizing local and global statistics to mitigate the data uncertainty from image noise and acquisition conditions in the pixel-level space. The experimental results on six benchmark iris datasets verify the effectiveness and generalization ability of the proposed method on same-sensor and cross-sensor recognition.