Cross-age Face Recognition Method Based on Feature Subspace Direct Sum

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Cross-age Face Recognition Method Based on Feature Subspace Direct Sum

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[1]Ye Jihua,Guo Qiyi,Jiang Aiwen,et al.Cross-age Face Recognition Method Based on Feature Subspace Direct Sum[J].Journal of Zhengzhou University (Engineering Science),2021,42(05):7-12.[doi:10.13705/j.issn.1671-6833.2021.05.002]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 42卷 Number of periods: 2021 05 Page number: 7-12 Column: Public date: 2021-09-10

Title:: Cross-age Face Recognition Method Based on Feature Subspace Direct Sum

Author(s):: Ye Jihua; Guo Qiyi; Jiang Aiwen; Li Xin;; School of Computer Information Engineering, Jiangxi Normal University;

Keywords:: face recognition; cross-age; multi-task; subspace direct sum; feature subspace

CLC:: -

DOI:: 10.13705/j.issn.1671-6833.2021.05.002

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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Last Update: 2021-10-11