International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Advancements in Human Action Recognition: Leveraging Public Datasets and Biometric-Based Authentication Systems

Abstract

Human action recognition HAR is one of the critical areas of research in computer vision with several applications in security, healthcare, and human-computer interaction. The datasets made available to the public are used in the development of the field because they offer crucial tools for model calibration, credibility checks, and determination of efficiency. This paper presents a brief and important overview of HAR datasets, based on the categories of atomic actions, behaviors, interactions, as well as group activities. From the datasets, KTH, NTU RGB+D, and UCF101, the impact on action recognition is analyzed. Further, we consider the use of a biometric-based password system to better understand a more secure and convenient way of operating password systems. In this paper, a strategy of combining biometric authentication methods with multi-factor security mechanisms is presented. The paper reveals the need for enhancing the performance of the biometric system to generate good, secure passwords and emphasizes the privacy issues and weaknesses of the system. Last of all, the paper presents the future directions and proposals for behavioral biometric and multi-modal approaches to enhance security.

Country : Iraq

1 Ameera S. Mahmood2 Yaseen Hikmat Ismaiel

  1. Department Computer Science, College of Computer and Mathematic Science, Mosul University, Iraq
  2. Department Computer Science, College of Computer and Mathematic Science, Mosul University, Iraq

IRJIET, Volume 9, Issue 8, August 2025 pp. 22-30

doi.org/10.47001/IRJIET/2025.908004

Full Paper
Download

References

  1. I.Adjabi, A. Ouahabi, A. Benzaoui, and A. Taleb-Ahmed, “Past, Present, and Future of Face Recognition: A Review,” Electronics, vol. 9, no. 8, 2020, doi: 10.3390/electronics9081188.
  2. U. Sumalatha, K. K. Prakasha, S. Prabhu, and V. C. Nayak, “A Comprehensive Review of Unimodal and Multimodal Fingerprint Biometric Authentication Systems: Fusion, Attacks, and Template Protection,” IEEE Access, vol. 12, pp. 64300–64334, 2024, doi: 10.1109/ACCESS.2024.3395417.
  3. H. A. Hussain and H. H. Abbas, “A Survey on Multi-biometric Fusion Approaches,” Kerbala J. Eng. Sci., vol. 03, no. 02, 2023.
  4. B. C. Arjun and H. N. Prakash, “Multimodal Biometric Recognition System Using Face and Finger Vein Biometric Traits with Feature and Decision Level Fusion Techniques,” Int. J. Comput. Theory Eng., vol. 13, no. 4, pp. 123–128, 2021, doi: 10.7763/IJCTE.2021.V13.1300.
  5. D. Cao, R. Liu, H. Li, S. Wang, W. Jiang, and C. X. Lu, “Cross Vision-RF Gait Re-identification with Low-cost RGB-D Cameras and mmWave Radars,” Proc. ACM Interactive, Mobile, Wearable Ubiquitous Technol., vol. 6, no. 3, 2022, doi: 10.1145/3550325.
  6. M. G. Morshed, T. Sultana, A. Alam, and Y. K. Lee, “Human Action Recognition: A Taxonomy-Based Survey, Updates, and Opportunities,” Sensors, vol. 23, no. 4, pp. 1–40, 2023, doi: 10.3390/s23042182.
  7. E. Balraj and T. Abirami, “Performance Improvement of Multibiometric Authentication System Using Score Level Fusion with Ant Colony Optimization,” Wirel. Commun. Mob. Comput., vol. 2022, 2022, doi: 10.1155/2022/4145785.
  8. [8]       N. Bala, D. R. Gupta, and A. Kumar, “Multimodal biometric system based on fusion techniques: a review,” Inf. Secur. J. A Glob. Perspect., pp. 1–49, Dec. 2021, doi: 10.1080/19393555.2021.1974130.
  9. R. R. Kumar et al., “Report on the Follow-Up To the Regional Implementation Strategy of the Madrid International Plan of Action on Ageing in Lithuania,” Front. Neurosci., vol. 14, no. 1, pp. 1–13, 2021.
  10. R. Raj and A. Kos, “An improved human activity recognition technique based on a convolutional neural network,” Sci. Rep.., vol. 13, no. 1, pp. 1–19, 2023, doi: 10.1038/s41598-023-49739-1.
  11. M. B. Shaikh and D. Chai, “RGB-D data-based action recognition: A review,” Sensors, vol. 21, no. 12, pp. 1–25, 2021, doi: 10.3390/s21124246.
  12. M. F. Bulbul, S. Tabussum, H. Ali, W. Zheng, M. Y. Lee, and A. Ullah, “Exploring 3d human action recognition using STACOG on multi-view depth motion maps sequences,” Sensors, vol. 21, no. 11, pp. 1–18, 2021, doi: 10.3390/s21113642.
  13. F. Shafizadegan, A. R. Naghsh-Nilchi, and E. Shabaninia, Multimodal vision-based human action recognition using deep learning: a review, vol. 57, no. 7. 2024. doi: 10.1007/s10462-024-10730-5.
  14. Y. Zhang et al., “Extract latent features of single-particle trajectories with historical experience learning,” Biophys. J., vol. 122, no. 22, pp. 4451–4466, 2023, doi: 10.1016/j.bpj.2023.10.023.
  15. A.Sergiyenko, P. Serhiienko, and M. Orlova, “Local Feature Extraction in Images,” Information, Comput. Intell. Syst., no. 2, 2021, doi: 10.20535/2708-4930.2.2021.244191.
  16. Y. Wei et al., “Elevating Skeleton-Based Action Recognition With Efficient Multi-Modality Self-Supervision,” ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc., no. April, pp. 6040–6044, 2024, doi: 10.1109/ICASSP48485.2024.10447178.
  17. A.Moly, L. Struber, and G. Charvet, “Hierarchical Hidden Markov Model for Online Decoding in Brain-Computer Interface,” pp. 1466–1470, 2024.
  18. M. Ameur, C. Daoui, and N. Idrissi, “Hierarchical hidden Markov models in image segmentation,” Sci. Vis., vol. 12, no. 1, pp. 22–47, 2020, doi: 10.26583/SV.12.1.03.
  19. J. S. Wibowo, E. N. Wahyudi, and H. Listiyono, “Performance Comparison of SVM, Naive Bayes, and Random Forest Models in Fake News Classification,” Eng. Technol. J., vol. 09, no. 08, pp. 4799–4804, 2024, doi: 10.47191/etj/v9i08.27.
  20. S. F. Ahmed et al., Deep learning modelling techniques: current progress, applications, advantages, and challenges, vol. 56, no. 11. Springer Netherlands, 2023. doi: 10.1007/s10462-023-10466-8.
  21. M. M. Taye, “Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions,” Computers, vol. 12, no. 5. 2023. doi: 10.3390/computers12050091.
  22. R. Luiz and P. Bueno, “ATTENTION-BASED VIEW : PAST, PRESENT AND FUTURE,” vol. 23, no. July 2023, pp. 1–41, 2024.
  23. R. Cui, A. Zhu, J. Wu, and G. Hua, “Skeleton-based Attention-aware Spatial-temporal Model for Action Detection and Recognition,” IET Comput. Vis., vol. 14, Feb. 2020, doi: 10.1049/iet-cvi.2019.0751.
  24. M. G. Morshed, T. Sultana, A. Alam, and Y. K. Lee, “Human Action Recognition: A Taxonomy-Based Survey, Updates, and Opportunities,” Sensors, vol. 23, no. 4, pp. 1–40, 2023, doi: 10.3390/s23042182.
  25. G. Diraco, G. Rescio, P. Siciliano, and A. Leone, “Review on Human Action Recognition in Smart Living: Sensing Technology, Multimodality, Real-Time Processing, Interoperability, and Resource-Constrained Processing,” Sensors, vol. 23, no. 11, pp. 1–26, 2023, doi: 10.3390/s23115281.
  26. E. Misini and U. Lajçi, “Biometric authentication,” Computer (Long. Beach, Calif), vol. 39, no. 2, pp. 96–97, 2022, doi: 10.1109/MC.2006.47.
  27. A.K. Jain, A. Ross, and K. Nandakumar, Introduction to Biometrics, Advances in Information Security, vol. 60, Springer, 2021. doi: 10.1007/978-1-4471-7307-2_1.
  28. M. Ganganna, “Review on Technology Advancements in Biometric Authentication Review on Technology Advancements in Biometric Authentication,” no. July 2024.
  29. P. Fernando, C. Liyanage, and C. Karunatilake, “Challenges and Opportunities in Password Management : A Review of Current Challenges and Opportunities in Password Management : A Review of Current Solutions,” no. August 2023, doi: 10.4038/sljssh.v3i2.96.
  30. N. A. Lal, S. Prasad, and M. Farik, “A Systematic Literature Review of the Types of Authentication,” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 12, no. 7, pp. 832–849, 2021, doi: 10.14569/IJACSA.2021.0120784.
  31. W. Go, K. Internet, S. Agency, and J. Kwak, “Construction of a secure two-factor user authentication system using fingerprint information and password,” no. April, 2014, doi: 10.1007/s10845-012-0669-y.
  32. L. E. Almeida, B. A. Fernández, D. Zambrano, and A. I. Almachi, “One-Time Passwords : A Literary Review of Different Protocols and Their Applications One-Time Passwords : A Literary Review of Different Protocols and Their Applications,” no. January 2024, doi: 10.1007/978-3-031-48855-9.
  33. Y. H. Ismail,  “Improved security using two levels of steganography,” AIP Conf. Proc., vol. 3264, no. 1, pp. 030010-1–030010-6, 2023, doi: 10.1063/5.0191873.
  34. Y. H. Ismail,Y.S.Yousif, “Using compression and encryption to provide secure image steganography,” AIP Conf. Proc., vol. 2398, no. 1, pp. 050033-1–050033-6, 2022, doi: 10.1063/5.0066466.
  35. Y. H. Ismail, A. A.Idres., “Proposed Method for Text Steganography,” Journal of Modern Computing and Engineering Research, vol. 3, no. 1, pp. 45–51, 2024.

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies