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Appl. Sci. 2018, 8(9), 1561; https://doi.org/10.3390/app8091561

PSI-CNN: A Pyramid-Based Scale-Invariant CNN Architecture for Face Recognition Robust to Various Image Resolutions

1
Center for Imaging Media Research, Korea Institute of Science and Technology, Seoul 02792, Korea
2
Department of HCI Robotics, University of Science and Technology, Daejeon 34113, Korea
*
Author to whom correspondence should be addressed.
Received: 13 August 2018 / Revised: 31 August 2018 / Accepted: 1 September 2018 / Published: 5 September 2018
(This article belongs to the Special Issue Advanced Intelligent Imaging Technology)
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Abstract

Face recognition is one research area that has benefited from the recent popularity of deep learning, namely the convolutional neural network (CNN) model. Nevertheless, the recognition performance is still compromised by the model’s dependency on the scale of input images and the limited number of feature maps in each layer of the network. To circumvent these issues, we propose PSI-CNN, a generic pyramid-based scale-invariant CNN architecture which additionally extracts untrained feature maps across multiple image resolutions, thereby allowing the network to learn scale-independent information and improving the recognition performance on low resolution images. Experimental results on the LFW dataset and our own CCTV database show PSI-CNN consistently outperforming the widely-adopted VGG face model in terms of face matching accuracy. View Full-Text
Keywords: face recognition; deep learning; pyramid-based approach; scale-invariant; low-resolution face recognition; deep learning; pyramid-based approach; scale-invariant; low-resolution
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Nam, G.P.; Choi, H.; Cho, J.; Kim, I.-J. PSI-CNN: A Pyramid-Based Scale-Invariant CNN Architecture for Face Recognition Robust to Various Image Resolutions. Appl. Sci. 2018, 8, 1561.

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