Abstract: Face recognition technology is the core technology of the subway ticketing system, which is related to the efficiency of people's ticket purchase. In order to improve people's experience of taking public transport, it is necessary to improve the performance of face recognition technology. In this study, the Back Propagation (BP) algorithm is used to optimize the parameters of the SoftMax classifier of the convolutional neural network, and the branch structure is added to the structure of the SphereFace-36 convolutional neural network to extract the local features of the face. Based on the improved neural network, the face recognition system of the subway automatic ticketing system is established. The results show that the area under the ROC curve is the highest for validation and identification of the optimization model; The recognition accuracy of the optimized model in different data sets is 1.0%, 0.7%, 1.1%, 0.9% and 0.6% higher than that of SphereFace-36 respectively, and its specificity is higher than that of SphereFace-36, with the maximum difference of 9%; The average accuracy of global feature extraction and recognition of the optimized network model is 83.01%. In the simulation experiment, the optimized model can accurately recognize facial features, which has high practical value and can be applied to the automatic ticketing system.

Shuang Wu, Xin Lin and Tong Yao, “Research on Face Recognition Technology of Subway Automatic Ticketing System based on Neural Network and Deep Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 13(12), 2022. http://dx.doi.org/10.14569/IJACSA.2022.01312123

@article{Wu2022,
title = {Research on Face Recognition Technology of Subway Automatic Ticketing System based on Neural Network and Deep Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.01312123},
url = {http://dx.doi.org/10.14569/IJACSA.2022.01312123},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {12},
author = {Shuang Wu and Xin Lin and Tong Yao}
}