利用判别采样的视频人脸亲属关系验证  

Discriminative sampling for video-based facial kinship verification

作　　者：王仕伟 闫海滨 Wang Shiwei;Yan Haibin(School of Automation,Beijing University of Posts and Telecommunications,Beijing 100876,China)

机构地区：[1]北京邮电大学自动化学院,北京100876

出　　处：《电子测量与仪器学报》2021年第8期12-19,共8页Journal of Electronic Measurement and Instrumentation

基　　金：国家自然科学基金(61976023);北京市自然科学基金(4174101)项目资助。

摘　　要：针对人脸视频数据中包含大量低质量数据和冗余信息的问题,提出了一种利用深度强化学习实现人脸视频的关键帧和困难负样本的判别采样,实现了视频人脸亲属关系的验证。设计了3个深度网络来实现特征的学习与关键帧的采样,其中基于残差网络(Resnet)训练的亲属关系验证网络(KVN)用于验证亲属分类;通过深度强化学习方法分别设计了两个采样网络:关键帧采样网络(KSN)和负样本采样网络(NESN),用于实现视频关键帧的选取以及负样本的筛选。实验结果表明,相比于现有的主流人脸亲属关系验证算法,方法有效提高了亲属关系验证识别率。In this paper,we propose a discriminative sampling method to select most effective samples via deep reinforcement learning for video-based kinship verification.Unlike most existing facial kinship verification methods which focus on extracting effective features with the random sampling strategy,we develop two deep reinforcement learning methods to select samples which are more suitable for learning discriminative features,so that the overall performance can be improved.Compared with the conventional kinship verification problem,video-based kinship verification has received less attention.However,this work has greater value in practical applications.When we try to use kinship verification to solve,for example,the problem of missing population,we often get video data.Compared with images,videos contain more information,and through reasonable use we will get better performance than image-based kinship verification.Specifically,our method uses three subnetworks to achieve the kinship verification task:one DQN-based sampling network to filter the key frame,one DQN-based sampling network to filter the negative sample,and one multi-layer convolutional network to verify the kin relationship.Experimental results on the KFVW datasets show the superiority of our proposed approach over the state-of-the-arts.

关 键 词：视频亲属关系验证 深度学习 强化学习 判别采样 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]