[1]叶继华,郭祺玥,江爱文,等.基于特征子空间直和的跨年龄人脸识别方法[J].郑州大学学报(工学版),2021,42(5):7-12.[doi:10.13705/j.issn.1671-6833.2021.05.002]
　Ye Jihua,Guo Qiyue,Jiang Aiwen,et al.Cross-age Face Recognition Method Based on Feature Subspace Direct Sum[J].Journal of Zhengzhou University (Engineering Science),2021,42(5):7-12.[doi:10.13705/j.issn.1671-6833.2021.05.002]
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基于特征子空间直和的跨年龄人脸识别方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
42
期数:
2021年5期
页码:
7-12
栏目:
出版日期:
2021-09-10

文章信息/Info

Title:
Cross-age Face Recognition Method Based on Feature Subspace Direct Sum
作者:
叶继华,郭祺玥,江爱文,黎欣
江西师范大学 计算机信息工程学院，江西 南昌 330022
Author(s):
Ye Jihua; Guo Qiyue; Jiang Aiwen; Li Xin;
College of Computer Information Engineering， Jiangxi Normal University， Nanchang 330022， China
关键词:
Keywords:
face recognition;  cross-age;  multi-task;  subspace direct sum;  feature subspace
DOI:
10.13705/j.issn.1671-6833.2021.05.002
文献标志码:
A
摘要:
针对跨年龄人脸识别任务,在同时进行人脸身份识别和年龄分类这两个任务的多任务卷积神经网络的基础上加入直和模块,提出了一种基于特征子空间直和的多任务卷积神经网络( FSDS-CNN ) 。该网络利用 2 个并行子网分别从深度特征中提取出身份相关特征和年龄相关特征,并对这 2 个相关特征所对应的特征子空间施加直和约束,使得身份相关特征与年龄相关特征尽可能无关。 通过多损失的联合监督学习,该网络可以获得随年龄变化鲁棒的年龄无关人脸身份特征。 分别在 Morph Album 2、CACD-VS 和 Cross-Age LFW 数据集上进行实验,其中在 CACD-VS 数据集中,所提方法的 AUC 最优值为99.7%；在 Cross-Age LFW 数据集中,所提方法在等错误率( EER) 和错误匹配率( FMR) 为 0.1 时的错误非匹配率(FNMR)上分别取得了最优值 10.1%和 10.2%。 同时在 3 个数据集上的实验均进行了消融对比实验以验证直和模块的有效性。 实验结果表明,身份特征与年龄特征的相关性被 FSDS-CNN 中的直和模块有效地降低,从而有效提升了模型跨年龄人脸识别的性能。
Abstract:
To solve cross-age face recognition tasks, this paper introduces the direct sum module on the basis of the multi-task convolutional neural network that simultaneously performs two tasks of face recognition and age classification, and proposes the feature subspace with direct sum multi-task convolutional neural network (FSDS-CNN). The network uses two parallel subnets to extract the identity-related feature and age-related feature from the deep feature, then the direct sum constraint is applied to the feature subspaces corresponding to these two related features, so that the correlation between identity-related feature and age-related feature is decreased as much as possible. Through the joint supervised learning of multiple loss functions, the network can obtain age-invariant face identity feature that is robust with age. Cross-age face recognition and verification experiments is conducted on three datasets (Morph Album 2, CACD-VS and Cross-Age LFW). In the CACD-VS dataset, the proposed method achieves the optimal result of 99.7% on the evaluation metric of AUC; in the Cross-Age LFW dataset, the method respectively achieves the optimal results of 10.1% and 10.2% on the evaluation metric of EER and FNMR when FMR is 0.1. At the same time, the ablation comparison experiments are conducted on the three datasets to verify the effectiveness of the direct sum module. The results show that the correlation between identity features and age features is effectively reduced by the direct sum module in FSDS-CNN, and then effectively improves the performance of cross-age face recognition.

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更新日期/Last Update: 2021-10-11