Ling-Yi Xu, Zoran Gajic. Improved Network for Face Recognition Based on Feature Super Resolution Method. International Journal of Automation and Computing. 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. https://doi.org/10.1007/s11633-021-1309-9

Improved Network for Face Recognition Based on Feature Super Resolution Method

doi: 10.1007/s11633-021-1309-9
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  • Author Bio:

    Lingyi Xu received the B. Sc. Degree in control theory and control engineering from the University of Science and Technology Beijing, China in 2014. In 2017, she received the M. Sc. degrees in control theory and control engineering at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China, and from Electrical and Computer Engineering with Rutgers University. Currently, she is pursuing her Ph. D. degree with the Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ, USA. Her current 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 Dipl.-Ing. (five year program) and Mgr.Sci. (two year program) degrees in electrical engineering from the University of Belgrade, the M. Sc. degree in applied mathematics, and the Ph. D. degree in systems science engineering under direction of Prof. Hassan Khalil from Michigan State University Department of Electrical Engineering and System Science in 1984. He was a Visiting Professor with Princeton University in 2003, and the American University of Sharjah in 2011. He is currently a Professor of Electrical and Computer Engineering with Rutgers University, 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 the IEEE Transactions on Automatic Control and the IFAC Automatica journals, 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 Simplified Language 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. His research interests are in controls systems, reinforcement learning, energy systems (fuel and solar cells, wind turbines, electric power grids), wireless communications, and networking. 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. E-mail: zgajic@rutgers.edu (Corresponding author) ORCID iD: 0000-0002-0187-6181

  • Received Date: 2021-02-17
  • Accepted Date: 2021-07-20
  • Available Online: 2021-07-30
  • 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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