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一种鲁棒的基于对抗结构的生物特征ROI提取方法

刘凤 刘浩哲 张文天 陈嘉树 沈琳琳 王磊

刘凤, 刘浩哲, 张文天, 陈嘉树, 沈琳琳, 王磊. 一种鲁棒的基于对抗结构的生物特征ROI提取方法. 自动化学报, 2020, 46(x): 1−14 doi: 10.16383/j.aas.c200156
引用本文: 刘凤, 刘浩哲, 张文天, 陈嘉树, 沈琳琳, 王磊. 一种鲁棒的基于对抗结构的生物特征ROI提取方法. 自动化学报, 2020, 46(x): 1−14 doi: 10.16383/j.aas.c200156
Liu Feng, Liu Hao-Zhe, Zhang Wen-Tian, Chen Jia-Shu, Shen Lin-lin, Wang Lei. A robust roi extraction method for biometrics using adversarial structure. Acta Automatica Sinica, 2020, 46(x): 1−14 doi: 10.16383/j.aas.c200156
Citation: Liu Feng, Liu Hao-Zhe, Zhang Wen-Tian, Chen Jia-Shu, Shen Lin-lin, Wang Lei. A robust roi extraction method for biometrics using adversarial structure. Acta Automatica Sinica, 2020, 46(x): 1−14 doi: 10.16383/j.aas.c200156

一种鲁棒的基于对抗结构的生物特征ROI提取方法

doi: 10.16383/j.aas.c200156
基金项目: 国家自然科学基金(91959108, 61672357), 深圳市基础研究基金(JCYJ20190808163401646, JCYJ20180305125822769), 腾讯“犀牛鸟“深圳大学青年教师科学研究基金资助
详细信息
    作者简介:

    刘凤:博士, 深圳大学助理教授, 西安电子科技大学学士、硕士, 在2014年, 获得香港理工大学计算机系的计算机科学博士学位. 主要研究方向为模式识别和图像处理以及相关技术在指纹领域中的应用. E-mail: feng.liu@szu.edu.cn

    刘浩哲:深圳大学硕士研究生, 主要研究领域为计算机视觉和模式识别. E-mail: liuhaozhe2019@email.szu.edu.cn

    张文天:深圳大学硕士研究生, 主要研究领域为模式识别和生物特征识别. E-mail: zhangwentianml@gmail.com

    陈嘉树:陕西科技大学本科生, 主要研究方向为计算机视觉与生物识别方向. E-mail: gaasyu.chan@gmail.com

    沈琳琳教授, 上海交通大学学士、硕士, 在诺丁汉大学获得博士学位. 现为深圳市“鹏城学者”特聘教授、英国诺丁汉大学计算机学院荣誉教授、澳门大学杰出访问学者; 广东省教育厅中英合作视觉信息处理实验室主任、深圳大学计算机视觉研究所所长、深圳大学医学影像智能分析与诊断研究中心主任. 研究方向主要为深度学习理论及其在人脸识别/分析以及医学图像分析上的应用. E-mail: llshen@szu.edu.cn

    王磊:副研究员, 博士生导师. 在西安电子科技大学获博士学位. 2011年至2012年期间就职于华为技术有限公司. 2014年至2015年在韩国仁川国立大学做博士后研究. 2016年加入中国科学院深圳先进技术研究院. 研究方向主要包括图像变换、计算机视觉、视觉语义理解、视频分析、深度学习等. E-mail: lei.wang1@siat.ac.cn

A Robust ROI Extraction Method for Biometrics using Adversarial Structure

Funds: Supported by the National Natural Science Foundation of China(61672357, 91959108), the Shenzhen Fundamental Research fund(JCYJ20190808163401646, JCYJ20180305125822769), Ten cent “Rhinoceros Birds”-Scientific Research Foundation for Young Teachers of Shenzhen University
  • 摘要: 感兴趣区域(Region of Interest, ROI) 提取在生物特征识别中, 常用于减少后续处理的计算消耗, 提高识别模型的准确性, 是生物识别系统中预处理的关键步骤. 针对生物识别数据, 本文提出了一种鲁棒的ROI提取方法. 方法使用语义分割模型作为基础, 通过增加全局感知模块, 与分割模型形成对抗结构, 为模型提供先验知识, 补充全局视觉模式信息, 解决了语义分割模型的末端收敛困难问题, 提高了模型的鲁棒性和泛化能力. 本文在传统二维(2D)指纹, 人脸, 三维(3D)指纹和指纹汗孔数据集中验证了方法的有效性. 实验结果表明, 相比于现有方法, 本文提出的ROI提取方法更具鲁棒性和泛化能力, 精度最高.
  • 图  1  基于PASCAL VOC 2011验证集的分割结果[2]. positive), 图像从左到右依次是, 原图, ROI的标签, 以及FCN[15]的分割结果. 第一行显示的案例是以马作为提取目标, 第二行显示的是飞行器提取案例.

    Fig.  1  Sample segmentation results[2] on the PASCAL VOC 2011 validation set. Columns(left to right): original images, ground-truth ROI, segmentations produced by FCN[15]. The first row shows the ROI extraction result for horse and the second row shows the result for aircraft extraction.

    图  2  拥有不同域信息的指纹图像: (a)指纹图像来自于FVCs[2123]. (b)是图像(a)的ROI区域. (c)指纹图像是来自数据集NIST 29[24]. (d)是(c)的ROI区域.

    Fig.  2  Samples of 2D Fingerprint images in different domains: (a). Images from FVCs[2123] (b). The labeled image of (a) marked with the ROI. (c). The fingerprints impressions in NIST29[24] (d). The artificial annotation for (c).

    图  7  基于全局损失函数的ROI提取模型

    Fig.  7  Overview of our proposed ROI extraction model.

    图  3  基于语义分割的ROI提取模型, 模型分为两部分: 基础网络和分割网络.

    Fig.  3  The flowchart of ROI extraction network based on semantic segmentation.

    图  4  在生物特征识别中, 基于语义分割的ROI提取模型存在的问题. 第一行是人脸提取的案例分析[35, 36], 第二行是指纹ROI提取的案例分析[2124].

    Fig.  4  ROI extraction issues we observe on biometrics cases. The first row shows the ROI extraction result for face[35, 36] and the second row presents the result for fingerprint ROI extraction[2124].

    图  5  像素级损失函数的失效情况, 分割结果(b)是分割结果(a)向左平移一个像素得到. 结果显示两个分割结果的交叉熵为264.80, L2为23.00.

    Fig.  5  Failure of pixel level loss functions: (a) translates one pixel to the left to get (b). Cross entropy between (a) and (b) is 264.80 and L2 is 23.00.

    图  6  基于对抗结构的全局损失模块的结构图

    Fig.  6  Adversarial structure based global perceptual loss module.

    图  8  3D指纹的横截面和对应ROI区域: (a). 标注了生物组织结构的指纹横截面图像 (b). 该横截面对应的ROI区域 (c). 指尖的生物结构[38].

    Fig.  8  An example of X-Z cross-section image labeled for 3D fingerprints:(a). The longitudinal(X-Z) fingertip image marked with biological structure. (b). The labeled image mark with the ROI. (c). Physical structure of human skin[38].

    图  9  ROI提取模型的收敛折线图. 第一行的评价指标为交并比, 第二行的评价指标为像素级准确率. 从左至右: 传统二维指纹的ROI提取, 人脸ROI提取和3D指纹ROI提取.

    Fig.  9  The convergent plots for ROI extraction model. The evaluation metric of first row is Mean IoU, that of second row is Pixel Acc..From left to right: 2D fingerprint, face and 3D fingerprint ROI extraction.

    图  10  不同训练次数下的2D指纹ROI, 人脸提取和3D指纹ROI的提取结果: 从左至右依次是不同的迭代次数的模型分割结果. 上面的一行是Baseline的分割结果, 下面的一行是本文方法的分割结果.

    Fig.  10  The Result for 2D fingerprint, face and 3D fingerprint ROI extraction with different iteration numbers. From left to right, there are the extraction results with different iteration numbers. The upper row corresponds to the extraction results of Baseline, and the lower row shows the results of the proposed method.

    图  11  人脸ROI提取和2D指纹ROI提取结果: 从左至右依次是原图, FCN, U-Net, PSPNet, Baseline和使用全局感知模块的ROI提取模型的结果. 第一行是人脸ROI提取的结果, 第二行是2D传统指纹的ROI提取结果.

    Fig.  11  The Result for face ROI extraction and 2D fingerprint ROI extraction. From left to right: the original image & the prediction of FCN, U-Net, PSPNet, Baseline and the proposed ROI extraction model using global perceptual loss module. The first row corresponds to face ROI extraction, and the second row shows the result of 2D traditional fingerprint ROI extraction.

    图  12  基于全局感知模块的3D指纹ROI提取结果: (a)原始的3D指纹图像[41]. (b)使用本文提出的方法, 针对(a)提取得到的ROI结果.

    Fig.  12  A set of images which show the ROI extraction result of our proposed method for 3D Fingerprint: (a). 3D fingerprint images obtained by OCT device[41]. (b). Effective structure of 3D fingerprint extracted by our proposed method.

    图  13  基于全局感知模块的汗孔提取结果: (a)原始的高精度指纹图像[14]. (b)使用本文提出的方法, 针对(a)提取得到的ROI结果.

    Fig.  13  The ROI extraction result of our proposed method for pore extraction: (a). High resolution fingerprint image[14]. (b). Fingerprint pores extracted by our proposed method.

    表  1  不同设置下的全局感知模块表现

    Table  1  Investigation of Global Perceptual Loss Module with Different Settings

    优化策略2D传统指纹(Pixel Acc.(%)/Mean IoU)人脸(Pixel Acc.(%)/Mean IoU)3D指纹(Pixel Acc.(%)/Mean IoU)
    本文方法Baseline本文方法Baseline本文方法Baseline
    损失函数IoU loss[2]92.07/0.863290.66/0.838092.05/0.857990.03/0.825496.97/0.885995.18/0.8640
    Lovasz loss[25]92.48/0.864893.14/0.882297.21/0.947596.71/0.938895.74/0.878895.69/0.8767
    L2 loss93.33/0.861389.33/0.821996.99/0.943496.90/0.942095.70/0.885094.14/0.8331
    CrossEntropy loss(base)92.58/0.860682.71/0.718097.06/0.942996.77/0.938996.13/0.897595.43/0.8719
    优化器AMSGrad[29]93.65/0.886392.39/0.867296.50/0.935396.17/0.928993.56/0.823090.45/0.7540
    Radam[30]92.72/0.869492.27/0.866596.72/0.939096.52/0.935095.77/0.880695.19/0.8676
    Adam(base)[27]92.58/0.860682.71/0.718097.06/0.942996.77/0.938996.13/0.897595.43/0.8719
    下载: 导出CSV

    表  2  2D指纹ROI提取实验结果

    Table  2  ROI Extraction Results of 2D Fingerprints

    FVCs vs. NIST29 Pixel Acc.(%)/MeanIoUNIST29 vs. FVCs Pixel Acc.(%)/MeanIoU平均值(Average) Pixel Acc.(%)/MeanIoU
    Mean and Variance based Method[13]76.71/0.685277.23/0.755176.97/0.7202
    Orientation based Method[12]75.37/0.753274.46/0.621374.92/0.6873
    Fourier based Method[13]65.45/0.634965.45/0.634965.45/0.6349
    PSPNet[17]87.74/0.800079.41/0.720983.58/0.7605
    FCN[15]87.20/0.793275.77/0.673681.49/0.7334
    U-Net[16]85.83/0.783976.46/0.725181.15/0.7545
    Baseline82.71/0.718073.12/0.734177.92/0.7261
    Baseline+Dense-CRF[48]90.33/0.783578.30/0.734784.32/0.7591
    本文方法92.58/0.860680.29/0.746986.44/0.8038
    本文方法+Dense-CRF94.67/0.885282.73/0.785288.70/0.8352
    下载: 导出CSV

    表  3  人脸提取案例实验结果

    Table  3  ROI Extraction Results of Face Images

    Pixel Acc(%). Mean IoU
    PSPNet[17] 93.62 0.8803
    FCN[15] 95.90 0.9212
    U-Net[16] 95.55 0.9147
    Baseline 96.77 0.9389
    Baseline+Dense-CRF[48] 95.77 0.9712
    本文方法 97.06 0.9429
    本文方法+Dense-CRF 96.41 0.9734
    下载: 导出CSV

    表  4  3D指纹的ROI提取结果.

    Table  4  ROI Extraction Results of 3D Fingerprints

    Pixel Acc.(%) Mean IoU
    PSPNet[17] 93.67 0.8296
    FCN[15] 94.62 0.8526
    U-Net[16] 94.82 0.8614
    Baseline 95.43 0.8719
    Baseline+Dense-CRF[48] 95.50 0.8718
    本文方法 96.13 0.8975
    本文方法+Dense-CRF 96.12 0.8898
    下载: 导出CSV

    表  5  指纹汗孔提取实验结果

    Table  5  Fingerprint Pore Extraction Results

    $ R_T$(%)$ R_F$(%)
    Gabor Filter[44]75.90(7.5)23.00(8.2)
    Adapt. Dog[14]80.80(6.5)22.20(9.0)
    DAPM[14]84.80(4.5)17.60(6.3)
    Xu等人[45]84.80(4.5)17.60(6.3)
    Labati等人[46]84.69(7.81)15.31(6.2)
    DeepPore[47]93.09(4.63)8.64(4.15)
    DeepPore$ ^*$96.33(6.57)6.45(17.22)
    Baseline97.48(9.63)7.57(5.85)
    本文方法98.30(9.2927)7.83(4.18)
    下载: 导出CSV
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  • 收稿日期:  2020-03-24
  • 录用日期:  2020-06-23

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