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Open AccessArticle

Face Recognition Using the SR-CNN Model

1
School of Computer Science, Yangtze University, Jingzhou 434023, China
2
National Demonstration Center for Experimental Electrical and Electronic Education, Yangtze University, Jingzhou 434023, China
3
School of Electronic and Information, Yangtze University, Jingzhou 434023, China
4
Key Laboratory of Exploration Technologies for Oil and Gas Resources, Yangtze University, Jingzhou 434023, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(12), 4237; https://doi.org/10.3390/s18124237
Received: 27 October 2018 / Revised: 27 November 2018 / Accepted: 28 November 2018 / Published: 3 December 2018
(This article belongs to the Special Issue Deep Learning-Based Image Sensors)
In order to solve the problem of face recognition in complex environments being vulnerable to illumination change, object rotation, occlusion, and so on, which leads to the imprecision of target position, a face recognition algorithm with multi-feature fusion is proposed. This study presents a new robust face-matching method named SR-CNN, combining the rotation-invariant texture feature (RITF) vector, the scale-invariant feature transform (SIFT) vector, and the convolution neural network (CNN). Furthermore, a graphics processing unit (GPU) is used to parallelize the model for an optimal computational performance. The Labeled Faces in the Wild (LFW) database and self-collection face database were selected for experiments. It turns out that the true positive rate is improved by 10.97–13.24% and the acceleration ratio (the ratio between central processing unit (CPU) operation time and GPU time) is 5–6 times for the LFW face database. For the self-collection, the true positive rate increased by 12.65–15.31%, and the acceleration ratio improved by a factor of 6–7. View Full-Text
Keywords: face matching; scale-invariant feature transform (SIFT); rotation-invariant texture feature (RITF); convolution neural network (CNN); graphic processing unit (GPU); parallel computing face matching; scale-invariant feature transform (SIFT); rotation-invariant texture feature (RITF); convolution neural network (CNN); graphic processing unit (GPU); parallel computing
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Yang, Y.-X.; Wen, C.; Xie, K.; Wen, F.-Q.; Sheng, G.-Q.; Tang, X.-G. Face Recognition Using the SR-CNN Model. Sensors 2018, 18, 4237.

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