Application of BP Neural Network in Face Recognition
Wenyang Yu, XianWei Wu, YuBin Yang
Available Online March 2014.
- https://doi.org/10.2991/mce-14.2014.120How to use a DOI?
- BP Network; Neural network; Image compression; Pattern recognition; Face recognition
- In this thesis, a BP neural network model is used to recognize the face direction, which established by simulating the human brain. The recognition model designed includes image compression, image sampling, input vector standardization, BP neural network and competition selection. Experimental results based On ORL face database. Then inputs them to train the neural network that consists of three layers, and the input, hidden and output layers have 42,70 and 40 neurons respectively. When training adjusts the parameters to ensure its performance and convergence speed. Lastly, a lot of the feature vectors are input to verify the model. By comparing the output and fact, it is found that the model is effective and reliable with an error rate of 6.5% merely. This demonstrates that there cognition model is simple and has a high recognition rate. Training speed and efficiency can be increased, and model classification performance can be improved obviously when these strategies are used cooperatively. Therefore, there cognition model and training strategies presented in this paper are practical.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Wenyang Yu AU - XianWei Wu AU - YuBin Yang PY - 2014/03 DA - 2014/03 TI - Application of BP Neural Network in Face Recognition BT - 2014 International Conference on Mechatronics, Control and Electronic Engineering (MCE-14) PB - Atlantis Press SP - 537 EP - 540 SN - 1951-6851 UR - https://doi.org/10.2991/mce-14.2014.120 DO - https://doi.org/10.2991/mce-14.2014.120 ID - Yu2014/03 ER -