Large Margin Coupled Mapping for Low Resolution Face Recognition
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 resolutio
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Graduate School at Shenzhen, Tsinghua University, Shenzhen, China [email protected] 2 Huazhong University of Science and Technology, Wuhan, China 3 Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing, China 4 Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information, Ministry of Education, Nanjing University of Science and Technology, Nanjing, China
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. Keywords: Coupled Mapping Low-resolution face recognition Margin Coupled Mapping FERET SCface
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1 Introduction A great number of achievements have been made in the area of automatic face recognition during last decades, especially under well-controlled circumstances. However, the performance of face recognition system in real world always degrades dramatically when the quality of input face images becomes poor, such as low-resolution. This is a specific concern in surveillance environment where the target is far from the sensor, resulting in low-resolution face images. To solve the low-resolution (LR) problem, a two-step framework is proposed following the intuition of first recovering lost detail information of LR face images and then applying traditional face recognition algorithms on recovered face images. In fact, most © Springer International Publishing Switzerland 2016 R. Booth and M.-L. Zhang (Eds.): PRICAI 2016, LNAI 9810, pp. 661–672, 2016. DOI: 10.1007/978-3-319-42911-3_55
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proposed two-step algorithms of LR face recognition apply super-resolution (SR) technique as the first step [1–5]. The super-resolved face images are then passed to the second general face recognition pipe. Through the development of last decade, there exists many SR algorithms to reconstruct high-resolution (HR) images from a single LR image [1] or multiple LR images [2]. In many real-world face recognition systems, the intuitive
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