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Convolutional Neural Network Super Resolution for Face Recognition in Surveillance Monitoring

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Articulated Motion and Deformable Objects (AMDO 2016)

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Abstract

Due to the importance of security in society, monitoring activities and recognizing specific people through surveillance video cameras play an important role. One of the main issues in such activity arises from the fact that cameras do not meet the resolution requirement for many face recognition algorithms. In order to solve this issue, in this paper we are proposing a new system which super resolves the image using deep learning convolutional network followed by the Hidden Markov Model and Singular Value Decomposition based face recognition. The proposed system has been tested on many well-known face databases such as FERET, HeadPose, and Essex University databases as well as our recently introduced iCV Face Recognition database (iCV-F). The experimental results show that the recognition rate is improving considerably after apply the super resolution.

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Acknowledgment

This work is supported Estonian Research Council Grant (PUT638) and the Spanish Project TIN2013-43478-P.

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Authors and Affiliations

  1. iCV Research Group, Institute of Technology, University of Tartu, Tartu, Estonia

    Pejman Rasti, Tõnis Uiboupin & Gholamreza Anbarjafari

  2. Computer Vision Center and University of Barcelona, Barcelona, Spain

    Sergio Escalera

Authors
  1. Pejman Rasti
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  2. Tõnis Uiboupin
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  3. Sergio Escalera
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  4. Gholamreza Anbarjafari
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Corresponding author

Correspondence to Pejman Rasti .

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Editors and Affiliations

  1. UIB-Universitat de les Illes Balears, Palma de Mallorca, Spain

    Francisco José Perales

  2. University of Surrey, Guildford, United Kingdom

    Josef Kittler

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Rasti, P., Uiboupin, T., Escalera, S., Anbarjafari, G. (2016). Convolutional Neural Network Super Resolution for Face Recognition in Surveillance Monitoring. In: Perales, F., Kittler, J. (eds) Articulated Motion and Deformable Objects. AMDO 2016. Lecture Notes in Computer Science(), vol 9756. Springer, Cham. https://doi.org/10.1007/978-3-319-41778-3_18

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  • DOI: https://doi.org/10.1007/978-3-319-41778-3_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41777-6

  • Online ISBN: 978-3-319-41778-3

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