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Relational Deep Feature Learning for Heterogeneous Face Recognition
Relational Deep Feature Learning for Heterogeneous Face Recognition
- 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.
- Subjects :
- Computer Science - Computer Vision and Pattern Recognition
Subjects
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