CASIA OpenIR  > 智能感知与计算
眼部生物特征图像预处理方法研究
王财勇
Subtype博士
Thesis Advisor孙哲南
2020-05-31
Degree Grantor中国科学院大学
Place of Conferral中国科学院自动化研究所
Degree Name工学博士
Degree Discipline模式识别与智能系统
Keyword虹膜识别 巩膜识别 眼部识别 虹膜分割 虹膜归一化 巩膜分割
Abstract

    眼部生物特征通常包括虹膜、巩膜、眼周、视网膜、眼动等生理或者行为特
征,其中,虹膜被认为是最稳定、准确和可依赖的生物特征之一,已经被广泛地
研究。当前,虹膜识别已经从受控场景逐渐向远距离、行进中、可见光、移动端
等多种复杂少约束场景发展,因而成像装置经常采集到许多低质量的虹膜图像,
为虹膜识别带来了严重的挑战。在捕获的虹膜图像中,通常也包含了完整的巩膜
区域,因此,进一步地挖掘巩膜特征的潜力,并与虹膜特征相互融合,取长补短,成为了提高复杂场景下眼部识别准确性和鲁棒性的一种有效方法。
    眼部图像预处理是眼部识别的一个关键科学问题,不仅定义了眼部特征提
取和比对的内容,也进行了图像的归一化和校正,减少了可能存在的类内差异,
因此对整个识别性能起着至关重要的作用。本文围绕着虹膜和巩膜这两种主要
的眼部生物特征,立足于为单模态的虹膜识别、巩膜识别或者多模态的虹膜和巩
膜融合识别提供准确、鲁棒的预处理结果以便进行后续的特征分析,重点研究面
向复杂场景下三种最基本的预处理操作,包括虹膜分割、虹膜归一化和巩膜分
割,最终提出对遮挡、照明、光照、旋转、运动、离焦等噪声鲁棒并且快速准确
的眼部图像预处理算法。本文的主要工作和贡献如下:
    1. 针对当前缺乏面向复杂场景下的大规模虹膜分割基准数据集的问题,本
文首先收集了涵盖不同的照明条件(近红外、可见光)、不同的成像传感器(虹
膜专业摄像头、普通摄像头、移动设备摄像头)、不同的成像距离(近距离、远
距离)、不同的人种(白人、亚洲人、黑人)、不同程度的用户配合、不同的噪
声类别(离焦、斜眼、镜面反射、模糊、遮挡、暗色虹膜、虹膜缺失、虹膜旋转
等)的多源虹膜数据集。进一步地,本文开发了简单高效的基于交互式的椭圆和
NURBS 曲线绘制算子的虹膜分割标注方法,摒弃了前人繁琐的、粗糙的基于关
键点拟合的方法,并结合新的虹膜掩膜精调软件 IrisLabel V0.0,完成了对基准数据集的虹膜掩膜和虹膜内外边界的精准标定。此外,本文也构建了全面完整的评测标准用于评估虹膜掩膜分割、虹膜内外边界定位以及对应的虹膜识别的准确
性、鲁棒性和泛化性以及模型复杂性。该基准数据集的建立为后续工作奠定了坚
实的数据基础,有利于推动复杂场景下鲁棒虹膜分割算法的发展。
    2. 针对当前基于深度学习的虹膜分割模型普遍缺乏鲁棒的虹膜内外边界定
位的问题,本文提出了一个端到端的基于多任务学习和注意力机制的完整虹膜
分割模型,同步预测了虹膜掩膜、虹膜外边界和瞳孔掩膜,从大量标记数据中学
习到了虹膜的纹理类别信息和边界形状信息。进一步地,通过利用虹膜掩膜与虹
膜内外边界的空间先验约束,提出了一个简单有效的后处理过程,最终实现了准
确鲁棒的参数化虹膜内外边界的定位和虹膜掩膜的分割,为复杂场景下的虹膜
识别奠定了良好的基础。
    3. 针对先前模型较重、参数较多、运行速度较慢、虹膜外边界预测较难的
问题,本文进一步地提出了一个端到端的基于多标签学习的轻量级虹膜分割模
型,并利用知识蒸馏的策略提升了轻量级模型的虹膜分割和定位性能。首先分析
了虹膜外边界定位的难点,提出了改用外边界填充的掩膜替代外边界进行预测
的方法,从而与虹膜掩膜、瞳孔掩膜一起构成了一个多标签学习的问题。其次,
基于 DeepLabV3 模型提出了重量级但性能优良的教师模型和轻量级但性能次优
的学生模型完成多标签学习。最后,利用了知识蒸馏的策略迁移教师网络的知识
更好地训练学生网络,从而提升了学生网络的虹膜分割和定位性能。提出的模型
高效、精确、鲁棒、轻量,有效解决了虹膜分割这一虹膜识别中的瓶颈问题。
    4. 以 Daugman 提出的 Rubber-Sheet 模型为基础,本文提出了基于空间转换网络的可微虹膜归一化模型,并综合考虑了多种归一化形式,实现了在网络内生成归一化的虹膜掩膜和归一化的虹膜图像,为后续的虹膜特征分析提供了准确
的虹膜区域。提出的模型可以使用标准的反向传播算法进行端到端的训练,为最
终构建端到端的虹膜识别系统奠定了良好的基础。
    5. 针对传统的基于模型驱动的方法和当前基于深度学习的方法在处理噪声
的巩膜图像分割时准确性差、鲁棒性低的问题,本文提出了注意力机制辅助的
U-Net 巩膜分割模型缓解这一难题。该模型通过嵌入不同类型的通道和空间注意
力模块到 U-Net 模型的中间瓶颈层和两侧的跳跃连接层,激活与巩膜有效区域
相关的特征权重,抑制与之无关的特征权重,从而提升了分割模型对于噪声的
鲁棒性,有效地提高了巩膜分割的精度,并在跨分辨率巩膜分割比赛 SSBC 2019
中赢得了冠军。
    总的来说,本文对眼部图像预处理的虹膜分割、虹膜归一化和巩膜分割等关
键问题进行了系统而深入的研究,提出了有效的解决方案,提高了识别系统在复
杂应用场景下的精度和可靠性。

Other Abstract

        Ocular biometrics usually includes iris recognition, sclera recognition, periocular recognition, retina recognition, eye movements recognition, etc., where iris recognition has been considered as one of the most stable, accurate and reliable biometric identification technologies and has been extensively studied. So far, iris recognition has been gradually developed from controlled scenarios to complex and less-constrained scenarios, e.g., at-a-distance, on-the-move, visible light illumination and using mobile devices. As a result, imaging devices often capture many low-quality iris images, which pose a serious challenge to iris recognition. In addition to the iris, a complete sclera region is usually contained in the captured iris image. Therefore, the accuracy and robustness of ocular recognition in complex scenarios are expected to be further improved by fusing
the iris and sclera traits.
       Ocular image preprocessing is a key scientific problem in ocular biometrics, which not only defines the content of eye feature extraction and matching, but also performs the normalization and correction to images to reduce possible intra-class differences, hence it directly affects the overall recognition performance. In this thesis, we focus on the two main ocular biometrics, i.e., iris biometrics and sclera biometrics, with the aim of producing accurate and robust ocular preprocessing results for both unimodal iris/sclera recognition and multimodal iris-sclera fusion recognition. Specifically, we study three key preprocessing operations in complex scenarios, i.e., iris segmentation, iris normalization and sclera segmentation. Several robust, fast and accurate ocular image preprocessing methods are finally proposed for dealing well with adverse noise factors, e.g., occlusion, illumination variations, specular reflections, rotation, motion blur and off-angle. Our main contributions are summarized as follows:
         1. In response to the lack of a large-scale iris segmentation benchmark dataset in complex scenarios, multiple iris datasets of various illumination (NIR, VIS), imaging  sensors (professional iris camera, ordinary camera, mobile iris camera), imaging distances (close-range, long-range), races (white, Asian, black), user cooperation levels, and noise factors (out-of-focus, gaze deviation, specular reflections, motion blur, occlusions, dark iris, absence of iris, iris rotation, etc.) are firstly collected. Furthermore, a simple and efficient iris segmentation annotation method based on interactive ellipse and NURBS curve drawing operators is developed, which discards the previous cumbersome and coarse key points-based fitting method, and combines a newly proposed iris mask fine-tuning software IrisLabel V0.0 for the annotation of iris mask and iris inner/outer boundary in the benchmark dataset. Besides, a complete and comprehensive evaluation protocol is built to evaluate the accuracy, robustness, and generalization of iris segmentation, localization, and recognition as well as the model complexity. The proposed benchmark dataset has laid a solid data foundation for the following-up work and is beneficial for the development of more advanced iris segmentation methods in complex scenarios.
         2. In response to the lack of a robust iris inner and outer boundaries localization technique for the existing deep learning based iris segmentation methods, an end-to-end multi-task learning and attention mechanism based iris segmentation model is proposed. It simultaneously predicts the iris mask, iris outer boundary and pupil mask for each image, and learns the iris texture category and boundary shape information from a large number of labeled datas. Furthermore, by exploiting the spatial priori constraints in the iris region, a simple yet effective post-processing method is proposed to achieve accurate and robust localization of parameterized iris inner and outer boundaries and refinement of predicted iris mask, which lays a good foundation for iris recognition in complex scenarios.
        3. In response to the problems of heavy parameters, slow running speed and difficult prediction of iris outer boundary in the former model, an end-to-end lightweight iris segmentation model based on multi-label learning is proposed, and a knowledge distillation strategy is adopted to improve the segmentation and localization performance of the lightweight model. In this method, iris outer boundary is first filled up to alleviate of the positive/negative imbalance problem in the previous iris outer boundary prediction, and thus collectively forms a multi-label learning problem with iris mask and pupil mask. To achieve the multi-label learning, the DeepLabV3 model is adopted, and the cumbersome but superior teacher model and the lightweight yet sub-optimal student model are therefore derived. Finally, a knowledge distillation strategy is used to transfer the knowledge from the teacher network to the student network, eventually improving the performance of the student network. The proposed lightweight model is accurate and robust, and provides a feasible solution to iris segmentation in iris biometrics.
         4. Based on Daugman's Rubber-Sheet model, a spatial transformer network based differentiable iris normalization model is proposed, and several normalization types are considered. The proposed model enables the normalized iris mask and normalized iris image to be generated within the network, which provides an accurate iris ROI region for subsequent iris feature analysis. Besides, it can also use the standard back propagation algorithm for end-to-end training, which lays a good foundation for the final end-to-end iris recognition system.
         5. In response to the problems of the poor accuracy and low robustness in traditional model-driven methods and current deep learning-based methods when dealing with noisy sclera images, an attention assisted U-Net model is proposed to solve these challenges. Several different types of attention modules in channel-wise and spatialwise are incorporated into the central bottleneck part or skip connection part of the original U-Net, helping the new model implicitly learn to suppress irrelevant regions while highlighting salient features which are useful for sclera segmentation. Enjoying these benefits, the proposed model effectively improves the robustness and accuracy of
sclera segmentation, and wins the Sclera Segmentation Benchmarking Competition in Cross-resolution Environment (SSBC 2019).
          In summary, the thesis systematically and deeply studies iris segmentation, iris normalization and sclera segmentation for ocular biometrics. A number of attempts are made and the accuracy and robustness of personal identification in complex scenarios
are significantly improved.

Pages178
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/39158
Collection智能感知与计算
Recommended Citation
GB/T 7714
王财勇. 眼部生物特征图像预处理方法研究[D]. 中国科学院自动化研究所. 中国科学院大学,2020.
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