Share:


Method for real time face recognition application in unmanned aerial vehicles

    Romualdas Jurevičius Affiliation
    ; Nikolaj Goranin Affiliation
    ; Justinas Janulevičius Affiliation
    ; Justas Nugaras Affiliation
    ; Ivan Suzdalev Affiliation
    ; Aleksandr Lapusinskij Affiliation

Abstract

Newly evolving threats to public safety and security, related to attacks in public spaces, are catching the attention of both law enforcement and the general public. Such threats range from the emotional misbehaviour of sports fans in sports venues to well-planned terrorist attacks. Moreover, tools are needed to assist in the search for wanted persons. Static solutions, such as closed circuit television (CCTV), exist, but there is a need for a highly-portable, on-demand solution. Unmanned aerial vehicles (UAVs) have evolved drastically over the past decade. Developments are observed not only with regards to flight mechanisms and extended flight times but also in the imaging and image stabilization capabilities. Although different methods for facial recognition have existed for some time, dealing with imaging from a moving source to detect the faces in the crowd and compare them to an existing face database is a scientific problem that requires a complex solution. This paper deals with real-time face recognition in the crowd using unmanned aerial vehicles. Face recognition was performed using OpenCV and Dlib libraries.

Keyword : unmanned aerial vehicle, drone, face recognition, real-time analysis, monitoring

How to Cite
[1]
Jurevičius, R., Goranin, N., Janulevičius, J., Nugaras, J., Suzdalev, I. and Lapusinskij, A. 2019. Method for real time face recognition application in unmanned aerial vehicles. Aviation. 23, 2 (Dec. 2019), 65-70. DOI:https://doi.org/10.3846/aviation.2019.10681.
Published in Issue
Dec 18, 2019
Abstract Views
89
PDF Downloads
58
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Buyse, M., Blavier, M., Buyse, M. & Marchand, M. (2017). U.S. Patent No. 15/051,166.

Dlib. (2018a). Dlib 18.6 released: Make your own object detector. Retrieved from http://blog.dlib.net/2014/02/dlib-186-re-leased-make-your-own-object.html

Dlib. (2018b). Modern C++ toolkit containing machine learning algorithms. Retrieved from http://dlib.net/

Edelman, G. J., & Aalders, M. C. (2018). Photogrammetry using visible, infrared, hyperspectral and thermal imaging of crime scenes. Forensic Science International, 292, 181-189. https://doi.org/10.1016/j.forsciint.2018.09.025

Go. (2019). The Go Programming Language. Retrieved from https://golang.org/

He, D., Chan, S., & Guizani, M. (2017). Drone-assisted public safety networks: The security aspect. IEEE Communications Magazine, 55(8), 218-223. https://doi.org/10.1109/MCOM.2017.1600799CM

Hjelmas, E. & Low, B. K. (2001). Face detection: A survey. Computer Vision and Image Understanding, 83, 236-274. https://doi.org/10.1006/cviu.2001.0921

Lee, S., & Kim, T. (2018). U.S. Patent No. 15/486,109. https://doi.org/10.1016/j.cca.2018.07.032

OcuSync. (2018). OcuSync DJI OcuSync Air System. Retrieved from https://store.dji.com/product/dji-ocusync-air-system

OpenCV. (2018a). Open Source Computer Vision Library, OpenCV team. Retrieved from https://opencv.org/

OpenCV. (2018b). OpenCV Cascade Classifier. Retrieved from https://docs.opencv.org/3.4/db/d28/tutorial_cascade_classi-fier.html

OpenFace. (2019). OpenFace: Free and open source face recognition with deep neural networks. Retrieved from https://cmusatyalab.github.io/openface/

Osuna, E., Freund, R. & Girosit, F. (1997). Training support vector machines: an application to face detection. Computer Vision and Pattern Recognition (pp. 130-136). IEEE.

Panta, F. J., Roman-Jimenez, G., & Sedes, F. (2018). Modelling metadata of CCTV systems and Indoor Location Sensors for automatic filtering of relevant video content. In 2018 12th International Conference on Research Challenges in Information Science (pp. 1-9). IEEE. https://doi.org/10.1109/RCIS.2018.8406677

Raspberry Pi Foundation. (2018a). Raspberry Pi3 Model B. Retrieved from https://www.raspberrypi.org/products/raspberry-pi-3-model-b/

Raspberry Pi Foundation. (2018b). Camera module v2. Retrieved from https://www.raspberrypi.org/products/camera-module-v2/

ResNet. (2019). Understanding and implementing architectures of ResNet and ResNeXt for state-of-the-art image classification: From Microsoft to Facebook. Retrieved from https://medium.com/@14prakash/understanding-and-implementing-architectures-of-resnet-and-resnext-for-state-of-the-art-image-cf51669e1624

Rowley, H. A., Baluja, S., & Kanade, T. (1998). Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1), 23-38. https://doi.org/10.1109/34.655647

VueJs. (2019). VueJs: The Progressive JavaScript Framework. Retrieved from https://vuejs.org/

Wang, T. (2014). Patent No. US 2014/0037278 A1. https://doi.org/10.1016/j.trprot.2014.09.001

Wang, Y.-Q. (2014). An analysis of the Viola-Jones face detection algorithm. Image Processing on Line, 4, 128-148. https://doi.org/10.5201/ipol.2014.104

Yang, G., & Huang, T. (1994). Human face detection in a complex background. Pattern Recognition, 27(1), 53-63. https://doi.org/10.1016/0031-3203(94)90017-5

Zhao, C. (2017). Patent No. US 2017/0123425 A1.