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Relational Deep Feature Learning for Heterogeneous Face Recognition

Relational Deep Feature Learning for Heterogeneous Face Recognition

Authors :
Cho, MyeongAh
Kim, Taeoh
Kim, Ig-Jae
Lee, Kyungjae
Lee, Sangyoun
Source :
IEEE Transactions on Information Forensics and Security, vol. 16, pp. 376-388, 2021
Publication Year :
2020

Abstract

Heterogeneous Face Recognition (HFR) is a task that matches faces across two different domains such as visible light (VIS), near-infrared (NIR), or the sketch domain. Due to the lack of databases, HFR methods usually exploit the pre-trained features on a large-scale visual database that contain general facial information. However, these pre-trained features cause performance degradation due to the texture discrepancy with the visual domain. With this motivation, we propose a graph-structured module called Relational Graph Module (RGM) that extracts global relational information in addition to general facial features. Because each identity's relational information between intra-facial parts is similar in any modality, the modeling relationship between features can help cross-domain matching. Through the RGM, relation propagation diminishes texture dependency without losing its advantages from the pre-trained features. Furthermore, the RGM captures global facial geometrics from locally correlated convolutional features to identify long-range relationships. In addition, we propose a Node Attention Unit (NAU) that performs node-wise recalibration to concentrate on the more informative nodes arising from relation-based propagation. Furthermore, we suggest a novel conditional-margin loss function (C-softmax) for the efficient projection learning of the embedding vector in HFR. The proposed method outperforms other state-of-the-art methods on five HFR databases. Furthermore, we demonstrate performance improvement on three backbones because our module can be plugged into any pre-trained face recognition backbone to overcome the limitations of a small HFR database.

Details

Database :
arXiv
Journal :
IEEE Transactions on Information Forensics and Security, vol. 16, pp. 376-388, 2021
Publication Type :
Report
Accession number :
edsarx.2003.00697
Document Type :
Working Paper
Full Text :
https://doi.org/10.1109/TIFS.2020.3013186