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Face recognition under low illumination based on convolutional neural network

Face recognition under low illumination based on convolutional neural network Deep learning algorithm based on convolutional neural network has been widely used in the field of computer vision. A method based on deep convolution neural network is proposed for face recognition under low illumination. Firstly, the multi-scale retinex is used to enhance the face image in low-light imaging. Then the processed signal is input into the four-layer depth convolution neural network. The classification model is generated by the iterative training of the neural network. Finally, the input face image is classified based on the classification model. Multi-scale retinex utilises the principle of human eye perception of object brightness. Convolutional neural network can achieve better convergence rate and accuracy in classification and recognition of face images. Experiments on YaleB dataset show that the proposed algorithm and network model have better recognition performance. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Autonomous and Adaptive Communications Systems Inderscience Publishers

Face recognition under low illumination based on convolutional neural network

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1754-8632
eISSN
1754-8640
DOI
10.1504/IJAACS.2020.110746
Publisher site
See Article on Publisher Site

Abstract

Deep learning algorithm based on convolutional neural network has been widely used in the field of computer vision. A method based on deep convolution neural network is proposed for face recognition under low illumination. Firstly, the multi-scale retinex is used to enhance the face image in low-light imaging. Then the processed signal is input into the four-layer depth convolution neural network. The classification model is generated by the iterative training of the neural network. Finally, the input face image is classified based on the classification model. Multi-scale retinex utilises the principle of human eye perception of object brightness. Convolutional neural network can achieve better convergence rate and accuracy in classification and recognition of face images. Experiments on YaleB dataset show that the proposed algorithm and network model have better recognition performance.

Journal

International Journal of Autonomous and Adaptive Communications Systems – Inderscience Publishers

Published: Jan 1, 2020

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