OPTIMISATION OF FACE DETECTION AND TRACKING ALGORITHMS IN REAL-TIME VIDEO STREAMS

Lijuan Yang,∗ Ying Li,∗ and Hui Zhao∗

References

  1. [1] C.´A. Casado and M.B. L´opez, Real-time face alignment:Evaluation methods, training strategies and implementationoptimization, Journal of Real-Time Image Processing, 18(6),2021, 2239–2267.
  2. [2] M. Alsmirat and N.J. Sarhan, Intelligent optimization forautomated video surveillance at the edge: A cross-layerapproach, Simulation Modelling Practice and Theory, 105,2020, 102171.
  3. [3] Z. Dong, J. Wei, X. Chen, and P. Zheng, Face detec-tion in security monitoring based on artificial intelli-gence video retrieval technology, IEEE Access, 8, 2020,63421–63433.
  4. [4] B. Rehman, W.H. Ong, A.C.H. Tan, and T.D. Ngo, Facedetection and tracking using hybrid margin-based ROItechniques, The Visual Computer, 36, 2020, 633–647.
  5. [5] P.S.F. Sheron, K.P. Sridhar, S. Baskar, and P.M. Shakeel,Projection-dependent input processing for 3D object recogni-tion in human-robot interaction systems, Image and VisionComputing, 106, 2021, 104089.
  6. [6] A. Mool, J. Panda, and K. Sharma, Optimizable face detectionand tracking model with occlusion resolution for high-qualityvideos, Multimedia Tools and Applications, 81(8), 2022,10391–10406.
  7. [7] M. Loey, G. Manogaran, M.H.N. Taha, and N.E.M. Khalifa,A hybrid deep transfer learning model with machine learningmethods for face mask detection in the era of the COVID-19pandemic, Measurement, 167, 2021, 108288.
  8. [8] J. Zheng, R. Ranjan, C.-H. Chen, J.-C. Chen, C.D. Castillo, andR. Chellappa, An automatic system for unconstrained video-based face recognition, IEEE Transactions on Biometrics,Behavior, and Identity Science, 2(3), 2020, 194–209.
  9. [9] D. Wodajo and S. Atnafu, Deepfake video detection usingconvolutional vision transformer, 2021, arXiv:2102.11126.
  10. [10] H. Yang and X. Han, Face recognition attendance systembased on real-time video processing, IEEE Access, 8, 2020,159143–159150.
  11. [11] A.R. Khan,, M. Harouni, S. Sharifi, S.A. Bahaj, and T. Saba,Face detection in close-up shot video events using video mining,Journal of Advances in Information Technology, 14(2), 2023,160–167.
  12. [12] P.J. Lu and J.-H. Chuang, Fusion of multi-intensity image fordeep learning-based human and face detection, IEEE Access,10, 2022, 8816–8823.
  13. [13] R. Singh, S. Saurav, T. Kumar, R. Saini, A. Vohra, and S.Singh, Facial expression recognition in videos using hybridCNN & ConvLSTM, International Journal of InformationTechnology, 15(4), 2023, 1819–1830.
  14. [14] Y. Wang, L Huang, and A.L. Yee, .Full-convolution Siamesenetwork algorithm under deep learning used in tracking of facialvideo image in newborns, The Journal of Supercomputing,78(12), 2022, 14343–14361.
  15. [15] S. Gupta, S.P. Gupta, S.A. Kumar, and M. Wasim, Robustmulti-sensor facial recognition in real time using NvidiaDeepStream, International Journal of Engineering Research &Technology, 11(1), 2022, 369– 373.
  16. [16] F. Majeed, F.Z. Khan, M. Nazir, Z. Iqbal, M. Alhaisoni,U. Tariq, M.A. Khan, and S. Kadry, Investigating theefficiency of deep learning based security system in a real-timeenvironment using YOLOv5, Sustainable Energy Technologiesand Assessments, 53, 2022, 102603.
  17. [17] C. Zaharia, V. Popescu, and F. Sandu, Hardware–software partitioning for real-time object detectionusing dynamic parameter optimization, Sensors, 23(10),2023, 4894.15
  18. [18] P.P. Oroceo, J.I. Kim, E.M.F. Caliwag, S.H. Kim, and W.Lim, Optimizing face recognition inference with a collaborativeedge–cloud network, Sensors, 22(21), 2022, 8371.
  19. [19] X. Zhang, X. Shi, Z. Zhang, Z. Wang, and L. Zhang, A DDQNpath planning algorithm based on experience classification andmulti steps for mobile robots, Electronics, 11(14), 2022, 2120.
  20. [20] Q. Qu, X. Li, Y. Zhou, J. Zeng, M. Yuan, J. Wang, J. Lv,K. Liu, and K. Mao, An improved reinforcement learningalgorithm for learning to branch, 2022, arXiv:2201.06213.
  21. [21] F. Georgios, B. Dimitrios, and K. Kolomvatsos, A perva-sive framework for human detection and tracking, 2023,arXiv:2303.11170.
  22. [22] C. Leblond-Menard and S. Achiche, Non-intrusive real timeeye tracking using facial alignment for assistive technologies,IEEE Transactions on Neural Systems and RehabilitationEngineering, 31, 2023, 954–961.
  23. [23] D. Mamieva, A.B. Abdusalomov, M. Mukhiddinov, and T.K.Whangbo, Improved face detection method via learning smallfaces on hard images based on a deep learning approach,Sensors, 23(1), 2023, 502.
  24. [24] F. Izhar, S. Ali, M. Ponum, M.T. Mahmood, H. Ilyas, and A.Iqbal, Detection & recognition of veiled and unveiled humanface on the basis of eyes using transfer learning, MultimediaTools and Applications, 82(3), 2023, 4257–4287.
  25. [25] Z. Wu, H. Fan, Y. Sun, and M. Peng, Efficient multi-objectiveoptimization on dynamic flexible job shop scheduling usingdeep reinforcement learning approach, Processes, 11(7), 2023,2018.
  26. [26] A. Girbau, T. Kobayashi, B. Renoust, Y. Matsui, and S. Satoh,Face detection, tracking, and classification from large-scalenews archives for analysis of key political figures, PoliticalAnalysis, 32(2), 2024, 221–239.
  27. [27] D.R.Q. Nortoyeva, Video image processing technologies, Worldof Scientific News in Science, 2(1), 2024, 522–528.
  28. [28] T. Xie, Improved single target identification tracking algorithmbased on IPSO-BP neural network, Applied Mathematics andNonlinear Sciences, 9(1), 2024, https://doi.org/10.2478/amns-2024-0336.
  29. [29] P. Prabhat, H. Gupta, and A.K. Vishwakarma, Face Detection:Present state and research directions, 2024, arXiv:2402.03796.
  30. [30] Wider face a face detection dataset, https://www.kaggle.com/datasets/iamprateek/wider-face-a-face-detection-dataset/data.
  31. [31] B. Li, H. Zhang, and X. Shi, A novel path planning for auv basedon dung beetle optimisation algorithm with deep Q-network,International Journal of Robotics and Automation, 40(1), 2025,65–73, DOI: http://dx.doi.org/10.2316/J.2025.206-1098.
  32. [32] P. Sivaprakash, S.S. Priya, K. Maheswari, B. Rubini, N.Karthikeyan, and B. Shuriya, Patent search classificationmodel for service robots field using deep learning approach,International Journal of Robotics and Automation, 40(1), 2025,15–22, DOI: http://dx.doi.org/10.2316/J.2025.206-0935.

Important Links:

Go Back