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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Autonomous and Adaptive Communications Systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
