CASIA OpenIR  > 毕业生  > 博士学位论文
面向开放场景的虹膜图像预处理与识别方法研究
卫建泽
Subtype博士
Thesis Advisor孙哲南
2022-05-20
Degree Grantor中国科学院大学
Place of Conferral中国科学院大学
Degree Discipline计算机应用技术
Keyword虹膜识别 虹膜分割 虹膜超分 采集不确定性
Abstract

新冠疫情在全球范围内的爆发极大地改变了人们的生活状态,积极地采取如佩戴口罩、避免接触在内的防疫措施已成常态;这些防疫措施在有效阻止新冠肺炎病毒蔓延的同时也影响了现有的身份认证技术,甚至使其面临失效的风险。为此,寻找当前防疫场景下可用的高性能身份认证技术成为一项迫在眉睫的任务,而虹膜识别技术的非接触式采集和高可靠性使其适用于当前的防疫场景。然而,虹膜识别技术的进一步推广需要突破其现有的采集限制,将虹膜识别技术从受控场景扩展到采集约束更少的开放场景中进行研究。开放场景中包含有大量的不确定采集因子,这些因子的高不确定性往往会造成成像质量不稳定和整体水平下滑,图像端的退化将会引发虹膜图像预处理和特征识别环节的连锁反应,进而影响整个系统的识别性能。针对开放场景下图像退化引起的挑战,本文立足于虹膜图像预处理和识别任务对虹膜识别技术展开研究。本文所取得的主要研究成果如下:

  1. 针对开放场景中不稳定成像对虹膜图像预处理的影响,提出了基于分布差异感知的虹膜元超分网络和基于预测差异感知的双边关联分割网络。第一个模型针对超分过程中训练数据和测试数据间的分布差异,将主干网络中的模型参数分解成由训练数据决定的固定参数和随输入数据变化的动态参数;动态参数由一个独立的元网络所控制,该网络通过感知输入图像和训练图像间的差异来调节动态参数,从而实现主干网络对输入图像的最佳适配。第二个模型针对虹膜分割中预测结果的不确定性,利用预测差异来估计分割不确定性,并利用该不确定性估计来引导模型优化,提升分割精度的同时减小了预测不确定性。此外,该模型还根据图像中人眼元素的视觉特征和空间布局构建了双边关联关系,以实现虹膜区域与非虹膜区域的有效区分。
  2. 针对跨光谱虹膜识别中多光谱图像间的分布差异问题,提出了基于设备独有性去除的异质虹膜识别算法。该算法利用 Gabor 函数作为先验知识来感知多光谱图像中的虹膜纹理,然后使用多分支网络将多光谱图像分解为面向分布对齐的基准分量和包含独有性信息的残差分量,并利用残差分量来改善基准分量以生成光谱不变特征。在两个跨光谱数据集和两个跨设备数据集上的实验表明了该算法有效地缩小了多光谱图像间和多设备图像间的分布差异,显著改善了识别性能。
  3. 针对开放场景中不确定的采集因子引发的特征模糊问题,提出了虹膜图像的概率隐表达并将其应用于欠标签虹膜识别。该表达依据虹膜图像在特征空间中的分布情况,使用概率分布而非传统的确定点来表示虹膜图像。其中身份信息被编码为概率分布的均值,而造成虹膜特征偏移的采集不确定性被表示为分布的方差,这种独立的编码方式避免了采集不确定性对识别的影响;基于该表达,同一目标类别的任意图像都对应为该分布上的一个采样点。进一步地,针对半监督和无监督设定下的识别挑战,本文基于概率隐表达提出了对比不确定性学习方法来从欠标签数据中学习有利于识别的知识。在六个虹膜数据集上的实验验证了概率隐表达的有效性,而有关半监督和无监督设定的实验结果表明无标签数据同样有利于虹膜识别模型的性能提升。
  4. 针对现有深度虹膜识别中对全局关联性建模缺失的问题,提出了多尺度关联测度分析并将其应用于降质虹膜图像识别。该方法从全局和局部两个尺度分析了虹膜区域间的关联性。全局关联性描述了所有虹膜区域之间的相关性,对局部遮挡有很好的鲁棒性;而局部关联性则聚焦近邻区域,对局部纹理细节更加敏感。在四个数据集上的实验结果表明,多尺度关联测度分析在多个性能指标上显著地优于对比方法;此外,而包括消融研究、可视化分析和遮挡识别在内的实验结果显示全局关联性和局部关联性是有助于提升识别精度的不同线索。
Other Abstract

The global outbreak of the COVID-19 has greatly changed people's daily life. It has been common measures to wear masks and avoid contact. These measures prevent the spread of the new crown pneumonia virus but hinder the usage of currently popular authentication technologies, such as face recognition, fingerprint recognition, ID card. Therefore, researchers are looking for a high-performance identity authentication technology available in the current epidemic scenario. The iris recognition technology attracts their attention due to its high reliability and contactless acquisition. However, the further promotion of iris recognition technology needs to break through its controlled acquisition condition and explore it in the open-world scenario with fewer acquisition constraints. There are a large number of uncertain acquisition factors in the open-world scenario. This acquisition uncertainty inevitably affects the preprocessing and recognition of iris images, leading to performance degeneration of the iris recognition system. To address these problems in the open-world scenario, we propose several approaches for preprocessing and recognizing iris images. The main contributions in the thesis are summarized as follows.

  1. Considering the impact of uncertain acquisition factors in the preprocessing of iris images, we propose the iris meta super-resolution network based on the distributional discrepancy perception and the bilaterally contextual segmentation network based on predictive discrepancy perception. The first model decomposes the model parameters of the backbone network into fixed parameters determined by the training data and dynamic parameters that change with the input sample. An independent meta network computes the dynamic parameters by perceiving the difference between the input and training images to fit the backbone network to the input image. The second model leverages the predictive discrepancy to estimate the uncertainty of the segmentation result and then utilizes this uncertainty estimate to make the optimizer pay more attention to the high-uncertainty area. In addition, the model learns the bilateral context according to the visual characteristics and spatial layouts of ocular components to segment out the iris region.
  2. We propose a heterogeneous iris recognition method based on device-specific band removal to reduce the distribution gap between samples from different spectra. This method first applies the Gabor function as the prior knowledge to perceive iris textures under different spectra. Then it adopts a trident network to decompose an image into a basic component that is about to be aligned and a residual component containing the device-specific band. The residual component helps the basic component to generate spectral-invariant features. Experiments on two cross-spectral iris datasets and two cross-sensor iris datasets show that our method reduces the distribution gaps between cross-spectral images and between cross-sensor images, significantly improving the recognition performance.
  3. To mitigate the feature ambiguity dilemma caused by the acquisition uncertainty in the open-world scene, we present a probabilistic implicit representation to describe iris images and leverage it to address iris recognition with insufficient labels. This representation applies a probabilistic distribution instead of the previous deterministic point to represent an iris image, in which the mean encodes the most likely identity feature of the iris image, while the variance encodes the data uncertainty from acquisition factors. Each captured image can be regarded as an instantiated iris feature sampled from the parametric probabilistic distribution. Furthermore, we propose contrastive uncertainty learning based on this probabilistic implicit representation for iris recognition under the semi-supervised and unsupervised settings. Experiments on six iris datasets demonstrate the effectiveness of probabilistic implicit representation, while experimental results on semi-supervised and unsupervised settings show that unlabeled data is also beneficial for performance improvement.
  4. To alleviate the local context modeling problem, we propose multi-scale contextual measures to mitigate the degraded recognition. This method analyzes the contexts between iris regions from global and local perspectives. The global context describes the relationships between all iris regions and is robust to local occlusion, while the local context measures the relationships with neighboring regions and is sensitive to local texture details. Experimental results on four datasets show that this method significantly outperforms compared approaches in multiple evaluation metrics. Moreover, extensive experimental results illustrate that global and local contexts are different clues critical for accurate iris recognition.
Pages148
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48649
Collection毕业生_博士学位论文
Recommended Citation
GB/T 7714
卫建泽. 面向开放场景的虹膜图像预处理与识别方法研究[D]. 中国科学院大学. 中国科学院大学,2022.
Files in This Item:
File Name/Size DocType Version Access License
卫建泽-面向开放场景的虹膜图像预处理与识(14652KB)学位论文 限制开放CC BY-NC-SA
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[卫建泽]'s Articles
Baidu academic
Similar articles in Baidu academic
[卫建泽]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[卫建泽]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.