Cho, MyeongAh Kim, Taeoh Kim, Ig-Jae Lee, Kyungjae Lee, Sangyoun 2024-01-19T15:33:43Z 2024-01-19T15:33:43Z 2022-01-25 2021-01 1556-6013 https://pubs.kist.re.kr/handle/201004/117562 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. English IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC Relational Deep Feature Learning for Heterogeneous Face Recognition Article 10.1109/TIFS.2020.3013186 1 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, v.16, pp.376 - 388 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 16 376 388 N scie scopus 000559432500008 2-s2.0-85089290170 Computer Science, Theory & Methods Engineering, Electrical & Electronic Computer Science Engineering Article DISCRIMINANT-ANALYSIS Heterogeneous face recognition relation embedding graph structured module face recognition