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Volume 18 Issue 6
December  2021
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Ling-Yi Xu, Zoran Gajic. Improved Network for Face Recognition Based on Feature Super Resolution Method. International Journal of Automation and Computing, vol. 18, no. 6, pp.915-925, 2021. https://doi.org/10.1007/s11633-021-1309-9
Citation: Ling-Yi Xu, Zoran Gajic. Improved Network for Face Recognition Based on Feature Super Resolution Method. International Journal of Automation and Computing, vol. 18, no. 6, pp.915-925, 2021. https://doi.org/10.1007/s11633-021-1309-9
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Improved Network for Face Recognition Based on Feature Super Resolution Method

doi: 10.1007/s11633-021-1309-9

  • Department of Electrical & Computer Engineering, Rutgers, The State University of New Jersey, Piscataway 08854, USA

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  • Author Bio:

    Ling-Yi Xu received the B. Sc. degree in control theory and control engineering from University of Science and Technology, China in 2014. She received the M. Sc. degree in control theory and control engineering at State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China in 2017, and received the M. Sc. degree in electrical and computer engineering from Rutgers, The State University of New Jersey, USA in 2017. Currently, she is a Ph. D. degree candidate with Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, USA. Her research interests include computer vision, machine learning, control systems and robotics. E-mail: lingyi.xu@rutgers.edu ORCID iD: 0000-0003-2984-7849

    Zoran Gajic received the Diploma in Engineering (five year program) and Magister of Science (two year program) degrees in electrical engineering from University of Belgrade, Serbia, received the M. Sc. degree in applied mathematics, and the Ph. D. degree in systems science engineering under direction of Professor Hassan Khalil from Department of Electrical Engineering and System Science, Michigan State University, USA in 1984. He was a visiting professor with Princeton University, USA in 2003, and the American University of Sharjah, UAE in 2011. He is currently a professor of Department of Electrical and Computer Engineering with Rutgers, The State University of New Jersey, where he has been involved in teaching linear systems and signals, controls, communication networks, optical networks, reinforcement learning, and electrical circuit courses since 1984. He has authored/co-authored close to 100 journal papers, primarily published in IEEE Transactions on Automatic Control and the IFAC Automatica, and eight books on linear systems and linear and bilinear control systems published by Academic Press, Prentice Hall, Marcel Dekker, Taylor and Francis, and Springer Verlag. His Prentice Hall book Linear Dynamic Systems and Signals was translated into the Chinese by Jiaotong University Press in 2004. His 1995 Academic Press book Lyapunov Matrix Equation in Systems Stability and Control was republished in 2008 by Dover Publications. Dr. Gajic has supervised 18 doctoral dissertations and 25 master theses. Eleven of his former doctoral students hold faculty positions with respected world universities. He has delivered four plenary lectures at international conferences and presented close to 150 conference papers. Dr. Gajic has served on editorial boards for nine journals and as a guest editor for six journal special issues. From 2003 to 2020, he was the Electrical and Computer Engineering Graduate Program Director. Presently, he serves on the American Association of University Professors National Council. Dr. Gajic is a Life Senior Master of the U. S. Chess Federation and a Master of the World Chess Federation.His research interests include control systems, reinforcement learning, energy systems (fuel and solar cells, wind turbines, electric power grids), wireless communications, and networking. E-mail: zgajic@rutgers.edu (Corresponding author) ORCID iD: 0000-0002-0187-6181

  • Received Date: 2021-02-17
  • Accepted Date: 2021-07-20
  • Publish Online: 2021-10-20
  • Publish Date: 2021-12-01
    Abstract
  • Low-resolution face images can be found in many practical applications. For example, faces captured from surveillance videos are typically in small sizes. Existing face recognition deep networks, trained on high-resolution images, perform poorly in recognizing low-resolution faces. In this work, an improved multi-branch network is proposed by combining ResNet and feature super-resolution modules. ResNet is for recognizing high-resolution facial images and extracting features from both high- and low-resolution images. Feature super-resolution modules are inserted before the classifier of ResNet for low-resolution facial images. They are used to increase feature resolution. The proposed method is effective and simple. Experimental results show that the recognition accuracy for high-resolution face images is high, and the recognition accuracy for low-resolution face images is improved.

     

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