Multi-scale patch based representation feature learning for low-resolution face recognition

Guangwei Gao

doi:10.1016/j.asoc.2020.106183

Multi-scale patch based representation feature learning for low-resolution face recognition

2020-05-01

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DOI：10.1016/j.asoc.2020.106183

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In practical video surveillance, the quality of facial regions of interest is usually affected by the large distances between the objects and surveillance cameras, which undoubtedly degrade the recognition performance. Existing methods usually consider the holistic representations, while neglecting the complementary information from different patch scales. To tackle this problem, this paper proposes a multi-scale patch based representation feature learning (MSPRFL) scheme for low-resolution face recognition problem. Specifically, the proposed MSPRFL approach first exploits multi-level information to learn more accurate resolution-robust representation features of each patch with the help of a training dataset. Then, we exploit these learned resolution-robust representation features to reduce the resolution discrepancy by integrating the recognition results from all patches. Finally, by considering the complementary discriminative ability from different patch scales, we try to fuse the multi-scale outputs by learning scale weights via an ensemble optimization model. We further verify the efficiency of the proposed MSPRFL on low-resolution face recognition by the comparison experiments on several commonly used face datasets. (C) 2020 Elsevier B.V. All rights reserved.

Keywords:

Face recognition

Low-resolution

Feature learning

Multi-scale patch

Multi-scale patch based representation feature learning for low-resolution face recognition

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