Improved Network for Face Recognition Based on Feature Super Resolution Method

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...

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Bibliographic Details
Published in:International journal of automation and computing 2021-12, Vol.18 (6), p.915-925
Main Authors: Xu, Ling-Yi, Gajic, Zoran
Format: Article
Language:English
Subjects:
CAE) and Design
Computer Applications
Computer-Aided Engineering (CAD
Control
Engineering
Face recognition
Facial recognition technology
Feature extraction
Feature recognition
High resolution
Image resolution
Mechatronics
Modules
Object recognition
Research Article
Robotics
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Summary: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.
ISSN:1476-8186
1751-8520
DOI:10.1007/s11633-021-1309-9