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人脸亲子关系验证研究综述

吴晓婷 冯晓毅 黄安 张雪毅 董晶 刘丽

吴晓婷, 冯晓毅, 黄安, 张雪毅, 董晶, 刘丽. 人脸亲子关系验证研究综述. 自动化学报, 2021, 47(x): 1−27 doi: 10.16383/j.aas.c201023
引用本文: 吴晓婷, 冯晓毅, 黄安, 张雪毅, 董晶, 刘丽. 人脸亲子关系验证研究综述. 自动化学报, 2021, 47(x): 1−27 doi: 10.16383/j.aas.c201023
Wu Xiao-Ting, Feng Xiao-Yi, Huang An, Zhang Xue-Yi, Dong Jing, Liu Li. Survey on facial kinship verification. Acta Automatica Sinica, 2021, 47(x): 1−27 doi: 10.16383/j.aas.c201023
Citation: Wu Xiao-Ting, Feng Xiao-Yi, Huang An, Zhang Xue-Yi, Dong Jing, Liu Li. Survey on facial kinship verification. Acta Automatica Sinica, 2021, 47(x): 1−27 doi: 10.16383/j.aas.c201023

人脸亲子关系验证研究综述

doi: 10.16383/j.aas.c201023
基金项目: 国家自然科学基金(61872379, 91846301), 湖南省重点领域研发计划(2019GK2131), 陕西省科技计划重点项目(2020GY-050)资助
详细信息
    作者简介:

    吴晓婷:西北工业大学电子信息学院与芬兰奥卢大学机器视觉与信号分析中心博士研究生. 主要研究方向为人脸亲子关系验证, 计算机视觉, 深度学习方向. E-mail:wuxt14@mail.nwpu.edu.cn

    冯晓毅:西北工业大学电子信息学院教授、博士生导师, 主要研究方向为计算机视觉, 图像处理, 雷达成像和识别. E-mail: fengxiao@nwpu.edu.cn

    黄安:国防科技大学系统工程学院硕士研究生. 主要研究方向为计算机视觉, 模式识别. E-mail: huangan99@nudt.edu.cn

    张雪毅:国防科技大学系统工程学院硕士研究生. 主要研究方向为计算机视觉, 模式识别. E-mail: zhangxy1998@nudt.edu.cn

    董晶:中国科学院自动化研究所智能感知与计算研究中心副研究员. 主要研究方向为模式识别与图像处理, 计算机视觉, 多媒体内容安全. E-mail: jdong@nlpr.ia.ac.cn

    刘丽:国防科技大学系统工程学院教授. 主要研究方向为图像理解, 计算机视觉, 模式识别. 本文通信作者. E-mail: liuli nudt@nudt.edu.cn

Survey on Facial Kinship Verification

Funds: Supported by National Natural Science Foundation of China (61872379, 91846301), Key Research and Development Program of Hunan Province (2019GK2131), Key Research and Development Program of Shaanxi(2020GY-050)
More Information
    Author Bio:

    WU Xiao-Ting Ph. D. candidate at the School of Electronics and Information, Northwestern Polytechnical University and Center for Machine Vision and Signal Analysis, University of Oulu, Oulu Finland. Her research interest covers facial kinship verification, computer vision and deep learning

    FENG Xiao-Yi Professor at the School of Electronics and Information, Northwestern Polytechnical University. Her research interest covers computer vision, image process, radar imagery and recognition

    HUANG An Master candidate at the Collage of Electronic Science, National University of Defense Technology. His research interest covers computer vision and pattern recognition

    ZHANG Xue-Yi Master candidate at the Collage of Electronic Science, National University of Defense Technology. His research interest covers computer vision and pattern recognition

    DONG Jing Associate research fellow at Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences. Her research interest covers pattern recognition, image processing, computer vision and digital image forensics

    LIU Li Professor at the College of System Engineering, National University of Defense technology. Her research interest covers image understanding, computer vision, and pattern recognition. Corresponding author of this paper

  • 摘要: 人脸亲子关系验证即通过给定的不同人的两幅人脸图像判断其是否具有亲子关系, 是计算机视觉和机器学习领域中一个重要的研究问题, 在丢失儿童寻找、社会媒体分析、图像自动标注等领域具有广泛的应用价值. 随着人脸亲子关系验证问题受到越来越多的关注, 其在多个方面都得到了相应的发展, 本文对人脸亲子关系验证方法做了综述整理. 首先, 简要介绍了人脸亲子关系验证在近十年的研究现状, 随后对问题进行了定义并讨论其面临的挑战. 接下来, 汇总了常用的亲子数据库, 对数据库属性做了详细的总结和对比. 然后, 对人脸亲子关系验证方法进行了分类总结、对比, 以及不同方法的性能表现. 最后, 展望了人脸亲子关系验证今后可能的研究方向.
  • 图  1  家庭中的关系示意图. 父母-子女关系是家庭关系的核心部件, 研究这一关系对促进人工智能加深对人类社会的理解有重要影响.

    Fig.  1  The illustration of kin relations. Parent-child is the core part of family relations. The study of this relationship has important implications for the promotion of artificial intelligence to deepen the understanding of human society.

    图  2  正样本对(具有亲子关系)和负样本对(不具有亲子关系)示意图

    Fig.  2  The illustration of positive pairs (with kin relations) and negative pairs (without kin relations).

    图  3  亲子关系任务示意图

    Fig.  3  The illustration of kinship related tasks.

    图  4  人脸亲子关系验证难点示意图.

    Fig.  4  The illustration of challenges of facial kinship verification.

    图  5  亲子数据集样本图.

    Fig.  5  Samples from kinship databases.

    图  6  亲子关系验证的一般流程.

    Fig.  6  The general framework of kinship verification.

    图  7  基于显著性区域的亲子关系验证算法框架图.

    Fig.  7  The architecture of saliency based kinship verification.

    图  8  LBP算法.

    Fig.  8  The algorithm of LBP.

    图  9  LBP可视化特征.

    Fig.  9  The visualization of LBP feature.

    图  10  彩色纹理特征提取方法.

    Fig.  10  The method of color-texture feature extraction.

    图  11  NRML算法框架图.

    Fig.  11  The architecture of NRML algorithm.

    图  12  大裕量多维度量学习算法示意图.

    Fig.  12  The illustration of LM3L.

    图  13  基于迁移子空间学习的人脸亲子关系验证方法示意图.

    Fig.  13  The illustration of transfer sub-space based kinship verification.

    图  14  基于基本CNN的亲子关系验证框架图

    Fig.  14  The architecture of the basic CNN based kinship verification.

    图  15  基于孪生网络的亲子关系验证框架.

    Fig.  15  The Siamese network based kinship verification.

    图  16  三元损失函数示意图.

    Fig.  16  The illustration of triplet loss.

    图  17  自编码器框架图

    Fig.  17  The architecture of Auto-Encoder.

    图  18  基于自编码器的亲子验证/图像合成框架图.

    Fig.  18  The architecture of auto-encoder based kinship verification/kin face synthesis.

    图  19  注意力机制示意图.

    Fig.  19  The illustration of attention mechanism.

    表  1  现有人脸亲子关系验证综述论文总结

    Table  1  The summary of the existing facial kinship verification survey papers.

    综述问题定义难点与挑战数据集总结人脸亲子关系验证算法性能分析未来的研
    究方向
    侧重点
    [21]$\times$$\times$$\checkmark$对早期的人脸亲子关系验证算法进行了总结.$\times$$\times$对人脸亲子关系验证问题的基础介绍.
    [22]$\times$$\checkmark$$\checkmark$对早期的人脸亲子关系验证算法进行了归类、总结.$\times$$\checkmark$对人脸亲子关系验证问题做了较为全面的总结.
    [23]$\checkmark$$\checkmark$$\checkmark$主要就亲子特征提取方法进行了对比.$\checkmark$$\checkmark$侧重阐述人脸亲子关系验证问题的衍生、定义、研究意义以及难点与挑战.
    [24]$\times$$\times$$\checkmark$对人脸亲子关系验证算法作了分类、汇总.$\checkmark$$\times$将人脸亲子关系验证任务和人脸衰老问题结合, 分析了其之间的相互关系.
    [25]$\checkmark$$\checkmark$$\checkmark$对人脸亲子关系验证算法作了分类、汇总.$\checkmark$$\checkmark$对亲子关系验证问题作了全面总结, 但是缺乏近期深度学习相关方法的汇总.
    [26]$\checkmark$$\checkmark$$\checkmark$对RFIW系列比赛中的方法进行了归纳.$\checkmark$$\checkmark$侧重于总结RFIW 系列比赛, 包括比赛任务介绍、现阶段性能对比及今后可能的研究方向.
    本文$\checkmark$$\checkmark$$\checkmark$对近十年来的亲子验证算法进行了全面的归纳、总结与分析.$\checkmark$$\checkmark$文章从问题的数学定义出发, 对人脸亲子关系验证问题进行了全方位的总结、剖析与展望.
    下载: 导出CSV

    表  2  人脸亲子关系验证方法的优缺点总结

    Table  2  The summary of advantages and disadvantages of the facial kinship verification methods.

    人脸亲子关系验证方法代表性方法针对的问题优势不足
    传统方法基于特征
    表示的
    基于显著特征[4951, 53-54], 手工特征[12, 5556, 58-59], 基于颜色的特征变换[11, 6264], 特征选择[6568]...为了解决亲子特征的有效表达问题, 从传统手工设计规则出发, 依据形状、颜色、纹理等特性描述亲子的相关特征.算法复杂度低, 特征提取速度快.对光照、人脸角度等因素敏感, 在自然条件下性能较差.
    基于度量
    学习的
    邻域驳斥度量学习[13, 72, 82], 基于双线性相似度[81, 83], 图构造[8788], 迁移子空间学习[34-35, 78], 余弦相似度[8990]...就一般距离度量不能很好的描述类内聚敛性和类间分离性问题, 提出度量学习优化亲子图像对间的距离衡量.优化亲子图像的距离度量方式, 使得亲子间距离缩小, 并且拉大非亲子间的距离.依赖于特征的有效
    表示, 距离优化程度
    有限.
    基于深度学习的基本卷积神经网络[30], 深度度量学习[15, 100, 101, 103, 106, 108], 基于自编码器[16, 17, 111, 113], 基于注意力机制[107]...传统方法固化于特定的规则, 很难适应于复杂情况, 深度网络针对问题自适应的学习数据的有效特征表示.依据损失函数训练网络参数, 特征表达能力强.需要大量的训练数据, 算法模型复杂, 训练时间长.
    下载: 导出CSV

    表  3  亲子关系数据库属性总结

    Table  3  characteristics of kinship databases.

    数据集, 年份数据集大小属性分辨率有无多
    幅图像/
    视频
    年龄
    变化
    有无家庭
    结构
    录制环境数据集特点针对的
    问题
    CornellKin[6], 2010300幅图像
    150对亲子
    图像100*100自然环境第一个亲子数据集1V1
    UB Kinface[34-35], 2011600幅图像
    200组亲子
    图像89*96自然环境包含年轻与年老的父母图像的数据集1V1
    UvA-NEMO
    Smile[31-32], 2012
    1240段视频视频1920*1080约束环境第一个亲子视频数据集1V1
    Family 101[37], 201314816幅图像
    101个家庭树
    图像120*150自然环境第一个有家庭结构的数据集1V1, 家庭
    识别
    KinFaceW-I[13], 20141066幅图像
    533对亲子
    图像64*64自然环境常用的数据集, 图像来自不同照片1V1
    KinFaceW-II[13], 20142000幅图像
    1000对亲子
    图像64*64自然环境常用的数据集, 图像来自同一照片1V1
    TSKinFace[38], 2015787幅图像
    1015组亲子
    图像64*64自然环境同时包含父亲和母亲的人脸图像2V1
    FIW[16], 201611193幅图像
    1000个家庭
    图像108*124自然环境当前规模最大的亲子数据集1V1, 2V1
    家庭识
    别, 检索}
    WVU[39], 2017904幅图像
    113对亲子
    图像32*32未知自然环境每人均有4幅图像不同的图片1V1
    KFVW[40], 2018836段视频
    418对亲子
    视频900*500自然环境第一个无表情约束视频数据集1V1
    FFVW[41], 2018300段视频
    100组亲子
    视频未知自然环境可用于三人组视频亲子关系验证2V1
    KIVI[42], 2019503段视频
    211个家庭
    视频未知自然环境无约束的亲子视频数
    据集
    1V1
    TALKIN[33], 2019800段视频
    400对亲子
    视频音频1920*1080自然环境第一个多模态数据集1V1
    表格中简写含义, 1V1表示两输入的亲子关系验证, 2V1表示三人组, 即父亲-母亲-孩子的亲子关系验证.
    下载: 导出CSV

    表  4  基于深度学习的亲子关系验证方法总结

    Table  4  The summary of deep learning based kinship verification

    方法特征图学习距离度量自编码器数据类型针对的问题
    DKV[14](MMSP2015)全局特征马氏距离图像针对亲子验证中的非线性问题, 提出用自编码器及深度度量学习改进图像特征和距离度量.
    CNN-Points[30](BMVC2015)局部特征神经网络图像对比于传统手工特征, 第一个提出端到端的深度学习算法, 并将面部分块作为网络输入.
    CMDAE[99](IJCAI2016)全局特征欧氏距离图像将父母和子女看做两个不同的域, 年轻时父母作为中间桥梁, 减小由年龄带来的差异性.
    SMCNN[15](ICIAR2016)全局特征L1范数图像将深度学习网络和度量学习结合, 提出使用L1范数作为距离度量, 端到端的更新网络参数.
    AdvNet[100](RFIW2017)全局特征欧氏距离图像大数据下的亲子关系验证问题, 提出使用多个对抗对比残差网络提取图像的多角度特征.
    DDML[101](TIP2017)全局特征欧氏距离图像针对传统度量学习大多为线性变换, 提出多特征的深度度量学习, 将亲子面部特征映射到非线性平面.
    CFT[102](ICCVW2017)全局特征欧氏距离图像针对亲子关系数据量小时, CNN网络容易过拟合问题, 提出人脸迁移学习方法.
    KinNet[103](RFIW2017)全局特征马氏距离图像引入Triplet损失函数训练网络, 并通过改变分辨率、加噪声等操作增加训练数据量.
    DRF[104](CCCV2017)局部特征神经网络图像对于大部分方法都基于相似度度量, 提出使用自编码器度量亲子间距离.
    SCCAE[16](ICCV2017)全局特征后验概率视频对于亲子间具有相似的表情, 通过表情匹配方法, 分析亲子间表情的关联性.
    Appearance+Shape[105]
    (ICIP2019)
    全局特征内积图像将面部的外观模型和几何模型进行分离, 从两个维度考虑亲子对的相似性.
    KML[106](Inf Fusion2019)全局特征余弦相似度图像针对隔代因素间的差异性, 提出紧凑型深度度量学习方法. 方法使用四输入比较其相似性与非相似性.
    Attention[107](PRL2019)局部特征神经网络图像针对亲子特征往往存在于人脸区域中, 并非全脸, 提出注意力机制的亲子关系验证方法.
    SMNAE[42](TIP2019)全局特征神经网络视频为解决亲子关系验证问题大都从静止图像分析, 提出空间-时间的亲子关系验证框架.
    Fusion Kinship[32](ICB2019)全局特征余弦相似度视频+语音针对现有亲子关系验证仅从视觉信息入手, 论文融合声音信号来提高模型的性能.
    AdvKin[108](TCYB2020)局部特征欧氏距离图像提出细粒度和更加严格的度量方法. 将人脸图像进行分块, 结合对比损失函数与对抗函数优化网络.
    GKR[109](ICME2020)全局特征神经网络图像针对如何进行特征融合及亲子关系推理. 对CNN输出的特征向量, 通过图结构分析两幅图像相似性.
    KINMIX[110](ICME2020)全局特征神经网络图像就数据量不足的问题, 在图像特征级上进行线性变换进行数据扩充, 数据可靠性高.
    下载: 导出CSV

    表  5  人脸亲子关系验证方法识别准确率(%)对比.

    Table  5  The comparison of accuracies (%)f or kinship verification methods.

    数据集方法类别方法特征度量/分类器FSFDMSMD平均值
    Cornell KinFace特征提取方法计算模型[6]低层次特征SVM72.954.673.861.365.7
    度量学习方法PDFL[72]LBP, SPLE, SIFTSVM74.869.177.566.171.9
    DMML[74]LBP, SPLE, SIFTSVM76.070.577.571.073.8
    KinFaceW-ID-CBFD[59]LBP阈值79.673.676.181.577.6
    特征提取方法特征选择[69]LBP, LPQ, SIFT高斯概率75.463.869.974.670.9
    PML-COV[58]HOG, LBPSVM91.084.387.1 90.288.2
    度量学习方法MNRML[13]LBP, SIFT, LE, TPLBPSVM72.566.566.272.069.9
    PDFL[72]LBP, SPLE, SIFTSVM73.567.566.173.170.1
    ESL[81]LBP双线性相似度81.771.169.674.374.1
    DMML[74]LBP, SPLE, SIFTSVM74.569.569.575.572.3
    WGEML[88]LBP, HOG, SIFT, CNNKNN78.573.980.681.978.7
    深度学习方法CNN-points[30]深度特征神经网络71.876.184.178.077.5
    SMCNN[15]深度特征阈值75.075.072.268.772.7
    DDML[101]深度特征阈值86.479.181.487.083.5
    AdvKin[108]深度特征阈值76.677.378.486.279.6
    Attention[107]深度特征神经网络81.285.978.285.282.6
    KinFaceW-II特征提取方法D-CBFD[59]LBP阈值81.076.277.479.378.5
    特征选择[69]LBP, LPQ, SIFT高斯概率82.476.276.673.270.0
    PML-COV[58]HOG, LBPSVM88.685.887.291.088.2
    度量学习方法MNRML[13]LBP, SIFT, LE, TPLBPSVM76.974.377.477.676.5
    PDFL[72]LBP, SPLE, SIFTSVM77.374.777.878.077.0
    LM3L[76]LBP, SIFT, LE, TPLBP阈值82.478.278.880.480.0
    ESL[81]LBP双线性相似度80.572.272.871.674.3
    DMML[74]LBP, SPLE, SIFTSVM78.576.578.579.578.3
    WGEML[88]LBP, HOG, SIFT, CNNKNN88.677.483.481.682.8
    深度学习方法CNN-points[30]深度特征神经网络81.989.492.489.988.4
    SMCNN[15]深度特征阈值79.075.085.078.079.3
    DDML[101]深度特征阈值87.483.883.283.084.3
    AdvKin[108]深度特征阈值91.685.290.292.489.9
    Attention[107]深度特征神经网络91.889.892.893.492.0
    TSKinFace特征提取方法颜色特征[10]BSIF-HSV阈值81.581.479.982.081.2
    度量学习方法WGEML[88]LBP, HOG, SIFT, CNNKNN90.389.891.490.490.5
    深度学习方法DDML[101]深度特征阈值88.587.087.987.887.8
    UBKinFace度量学习方法DMML[74]LBP, SPLE, SIFTSVM年轻父母-孩子年老父母-孩子72.3
    74.570.0
    FIW深度学习方法AdvKin[108]深度特征阈值68.867.867.369.968.5
    SmileSCCAE[16]自编码器隐含层阈值93.493.892.293.693.3
    KIVISMNAE[42]自编码器隐含层SVM80.081.877.892.383.0
    TALKIN模态融合[33]深度特征阈值80.070.573.572.574.1
    下载: 导出CSV
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  • 收稿日期:  2020-12-09
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