Design of civil aviation security check passenger identification system based on residual convolution network


  • Ning Zhang Guangzhou Civil Aviation College, Guangzhou, China
  • Youcheng Liang Guangzhou Civil Aviation College, Guangzhou, China
  • Loknath Sai Ambati Indiana University image/svg+xml



Residual convolution network, Civil aviation security check, Passenger identification, System design, A transmission control unit, Image acquisition module


INTRODUCTION: A civil aviation security check passenger identification system based on residual convolution network is designed to improve the efficiency of airport passenger security check service.

OBJECTIVES: The system uses the basic resource layer to provide communication and configuration services, collects the basic information of passengers, the images of passengers' faces and whole body, and the images of baggage security X-ray machine through the data layer, and stores the collected results in the unstructured database;

METHODS: The image processing module of the business service layer calls the data in the database, and takes the STM32F103VBT6 microprocessor as the image processing control chip to complete the image data processing. The person, baggage, X-ray machine image and passenger basic information are associated through the person, baggage and X-ray machine information binding service module, and the association results are uploaded to the person and certificates integration unit of the client application layer.

RESULTS: The face recognition module identifies the passenger identity through the residual convolution network with the attention mechanism, and realizes the ReID identification of passengers and baggage and the association of people and baggage through the transmission control unit.

CONCLUSION: The experimental results show that the system can accurately identify the identity of civil aviation security passengers, and the identification efficiency of security passengers can reach more than 27 frames per second.


Yu, J. (2022). Short-term airline passenger flow prediction based on the attention mechanism and gated recurrent unit model. Cognitive Computation, 14(2), 693-701.

Yang, C. , Wang, X. & Mao, S. (2020). Rfid-pose: vision-aided three-dimensional human pose estimation with radio-frequency identification. IEEE Transactions on Reliability, 70(3), 1218-1231.

Juneja, K. , & Rana, C. (2021). An extensive study on traditional-to-recent transformation on face recognition system. Wireless Personal Communications, 118(2), 3075-3128.

Liu, S., Li, Y., & Weina Fu. (2022) Human-centered attention-aware networks for Action recognition, International Journal of Intelligent Systems, online first, doi: 10.1002/int.23029.

Liu, S., Wang, S., Liu, X., Lin C. T., & Lv, Z. (2021) Fuzzy Detection aided Real-time and Robust Visual Tracking under Complex Environments. IEEE Transactions on Fuzzy Systems, 29(1), 90-102.

Wang, Q. , Ismail, K. N.& Breckon, T. P. (2020). An approach for adaptive automatic threat recognition within 3d computed tomography images for baggage security screening. Journal of X-Ray Science and Technology, 28(1), 35-58.

Zhang, Z. , Li, H. F. , & Li, M. Z. (2020). Research on YOLO Algorithm in Abnormal Security Images. Computer Engineering and Applications, 56(21), 187-193.

Sadeghzadeh, A. , & Ebrahimnezhad, H. . (2020). Pose-invariant face recognition based on matching the occlusion free regions aligned by 3d generic model. IET Computer Vision, 14(5), 268-277.

Gunawan, K. W. , Halimawan, N. , & Suharjito. (2021). Lightweight end to end pose-robust face recognition system with deep residual equivariant mapping. Procedia Computer Science, 179(2), 648-655.

Liu, S., Fu, W., & Zhao, W. (2013) A Novel Fusion Method by Static and Moving Facial Capture, Mathematical Problems in Engineering, 2013: 503924.

Kim, J. , Ra, M. , & Kim, W. Y. (2020). A dcnn-based fast nir face recognition system robust to reflected light from eyeglasses. IEEE Access, 8, 80948-80963.

Mastalerz, M. W. , Malinowski, A. , Kwiatkowski, S. , Niegula, A. , & Wieczorek, B. (2020). Passenger bibo detection with iot support and machine learning techniques for intelligent transport systems. Procedia Computer Science, 176, 3780-3793.

Zhen, G. Y. , Cao, F. , Chen, J. J. , & Jia, X. Z. (2021). Design of low power image acquisition system based on Hi3516D. Application of Electronic Technique,47(7):102-105.

Liu, Y. , Yang, B. , Gu, P. , Wang, X. , & Zong, H. (2020). 50x five-group inner-focus zoom lens design with focus tunable lens using gaussian brackets and lens modules. Optics Express, 28(20), 29098.

Huang, J. C. , Liu, C. S. , & Tsai, C. Y. (2021). Calibration procedure of camera with multifocus zoom lens for three-dimensional scanning system. IEEE Access, 9, 106387-106398.

Qu, W. , Xu, Z. , Luo, B. , Feng, H. & Wan, Z. (2020). Pedestrian re-identification monitoring system based on deep convolutional neural network. IEEE Access, 8, 86162-86170.

Chang, Z. W. , Pu, W. , Wu, J. , Huang, K. C. , Xiong, X. Z. , & Chen, M. J. (2022). An OpenPose-based Residual Network for Electric Worker's Wearable Device Recognition. Telecommunication Engineering, 62(1):31-38.

Cai, Q. , Li, H. Y. , Li, N. , & Liu, X. L. . (2021). Video Object Detection with Temporal Information and Attention Mechanism. Computer Simulation, 38(12): 380-385.

Huang, G. , Gong, Y. , Xu, Q. , Wattanachote, K. , & Luo, X. (2020). A convolutional attention residual network for stereo matching. IEEE Access, 8, 50828-50842.

Liu, X., Chen, S., Song, L., Woźniak, M., & Liu, S. (2021) Self-attention Negative Feedback Network for Real-time Image Super-Resolution, Journal of King Saud University -Computer and Information Sciences, online first, doi: 10.1016/j.jksuci.2021.07.014

Zhang, Y. D., Dong, Z., Wang S. H., Yu, X., Yao, X., Zhou, Q., Hu, H., Li, M., Carmen, J. M., Ramirez, J., Martinez, F. & Gorriz, J., M. (2020) Advances in multimodal data fusion in neuroimaging: overview, challenges, and novel orientation, Information Fusion, 64: 149-187

Wang, S., Govindaraj, V. V., Górriz, J. M., Zhang, X. & Zhang, Y. D. (2021) Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network, Information Fusion, 67: 208-229

Wang, S., Celebi, M. E., Zhang, Y. D., Yu, X., Lu, S., Yao, X., Zhou, Q., Miguel, M. G., Tian, Y., Gorriz, J., M. & Tyukin I. (2021) Advances in data preprocessing for biomedical data fusion: an overview of the methods, challenges, and prospects, Information Fusion, 76: 376-421




How to Cite

Zhang N, Liang Y, Ambati LS. Design of civil aviation security check passenger identification system based on residual convolution network. EAI Endorsed Scal Inf Syst [Internet]. 2022 Nov. 3 [cited 2023 Feb. 5];10(2):e12. Available from: