Degrade or super-resolve to recognize? Bridging the domain gap for cross-resolution face recognition

Grm, Klemen (author), Özata, Berk Kemal (author), Kantarcı, Alperen (author), Štruc, Vitomir (author), Ekenel, Hazim Kemal (author)

In this work, we address the problem of cross-resolution face recognition, where a low-resolution probe face is compared against high-resolution gallery faces. To address this challenging problem, we investigate two approaches for bridging the quality gap between low-quality probe faces and high-quality gallery faces. The first approach focuses on degrading the quality of high-resolution gallery images to bring them closer to the quality of the probe images. The second approach involves enhancing the resolution of the probe images using face hallucination. Our experiments on the SCFace and DroneSURF datasets reveal that the success of face hallucination is highly dependent on the quality of the original images, since poor image quality can severely limit the effectiveness of the hallucination technique. Therefore, the selection of the appropriate face recognition method should consider the quality of the images. Additionally, our experiments also suggest that combining gallery degradation and face hallucination in a hybrid recognition scheme provides the best overall results for cross-resolution face recognition with relatively high-quality probe images, while the degradation process on its own is the more suitable option for low-quality probe images. Our results show that the combination of standard computer vision approaches such as degradation, super-resolution, feature fusion, and score fusion can be used to substantially improve performance on the task of low resolution face recognition using off-the-shelf face recognition models without re-training on the target domain.

• Language: English
• Keywords: biometrics, image processing, machine Learning
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FE - Faculty of Electrical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: Str. 10542-10558
• Numbering: Vol. 13
• PID: 20.500.12556/RUL-167925
• UDC: 004.93:57.087.1
• ISSN on article: 2169-3536
• DOI: 10.1109/ACCESS.2025.3527236
• COBISS.SI-ID: 229666051
• Publication date in RUL: 20.03.2025

Record is a part of a journal

• Title: IEEE access
• Publisher: Institute of Electrical and Electronics Engineers
• ISSN: 2169-3536
• COBISS.SI-ID: 519839513

Secondary language

• Language: Slovenian
• Keywords: biometrija, procesiranje slik, strojno učenje

Projects

• Funder: SCIENTIFIC AND TECHNOLOGICAL RESEARCH COUNCIL OF TÜRKIYE (TUBITAK)
• Project number: 120N011
• Name: Low Resolution Face Recognition (FaceLQ)

• Funder: ARRS - Slovenian Research Agency
• Project number: P2-0250
• Name: Metrologija in biometrični sistemi