Abstract
Traditional face recognition algorithms can achieve significant performance under well-controlled environments. However, these algorithms perform poorly when the resolution of the face images varies. A two-step framework is proposed to solve the resolution problem through adopting super-resolution (SR) and performing face recognition on the super-resolved face images. However, such method usually has poor performance on recognition tasks as SR focuses more on visual enhancement, rather than classification accuracy. Recently, Coupled Mapping (CM) has been introduced into face recognition framework across different resolutions, which learns a common feature subspace for both high-resolution (HR) and low-resolution (LR) face images. In this paper, inspired by maximum margin projection, we propose Large Margin Coupled Mapping (LMCM) algorithm, which learns projections to maximize the margin between distance of between-class subjects and distance of within-class ones in the common space. Experiments on public FERET and SCface databases demonstrate that LMCM is effective for low-resolution face recognition.
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Acknowledgement
This work is partially supported by the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information (Nanjing University of Science and Technology), Ministry of Education (Grant No. 30920140122006).
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Zhang, J., Guo, Z., Li, X., Chen, Y. (2016). Large Margin Coupled Mapping for Low Resolution Face Recognition. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_55
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DOI: https://doi.org/10.1007/978-3-319-42911-3_55
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