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Reseach Article

A Comprehensive Review of Keystroke Dynamics and Human Gait Analysis in Biometric Authentication

by Sandip Dutta, Soumen Roy, Utpal Roy
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 63
Year of Publication: 2025
Authors: Sandip Dutta, Soumen Roy, Utpal Roy
10.5120/ijca2025924454

Sandip Dutta, Soumen Roy, Utpal Roy . A Comprehensive Review of Keystroke Dynamics and Human Gait Analysis in Biometric Authentication. International Journal of Computer Applications. 186, 63 ( Jan 2025), 1-6. DOI=10.5120/ijca2025924454

@article{ 10.5120/ijca2025924454,
author = { Sandip Dutta, Soumen Roy, Utpal Roy },
title = { A Comprehensive Review of Keystroke Dynamics and Human Gait Analysis in Biometric Authentication },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2025 },
volume = { 186 },
number = { 63 },
month = { Jan },
year = { 2025 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number63/a-comprehensive-review-of-keystroke-dynamics-and-human-gait-analysis-in-biometric-authentication/ },
doi = { 10.5120/ijca2025924454 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-01-31T17:28:30.701196+05:30
%A Sandip Dutta
%A Soumen Roy
%A Utpal Roy
%T A Comprehensive Review of Keystroke Dynamics and Human Gait Analysis in Biometric Authentication
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 63
%P 1-6
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The integration of keystroke dynamics and gait analysis has emerged as a promising approach in the field of biometric authentication, offering enhanced accuracy and reliability. This paper reviews the current state of research in these modalities, highlighting advancements in sensor technology and analytical methods. By combining timing and sensory features, multimodal systems achieve lower Equal Error Rates (EER), demonstrating significant improvements over unimodal approaches. The review includes a comprehensive analysis of studies that have explored this integration, providing insights into the methodologies employed and their effectiveness. Key findings indicate that the fusion of keystroke dynamics and gait analysis not only enhances authentication accuracy but also offers robust and statistically significant results. This paper underscores the potential of these integrated systems to advance biometric technologies, paving the way for future research and applications in security and user identification.

References
  1. Mohammed Abuhamad, Tamer Abuhmed, David Mohaisen, and Daehun Nyang. AUToSen: Deep-learning-based implicit continuous authentication using smartphone sensors. IEEE Internet of Things Journal, 7(6):5008–5020, jun 2020.
  2. M.I. Ahmad,W.L.Woo, and S.S. Dlay. Multimodal biometric fusion at feature level: Face and palmprint. Communication Systems Networks and Digital Signal Processing (CSNDSP), 2010 7th International Symposium on, 2010.
  3. Kalsoom Fatima, Sumbal Nawaz, and Sobia Mehrban. Biometric Authentication in Health Care Sector: A Survey. In 3rd International Conference on Innovative Computing, ICIC 2019, 2019.
  4. Mohamed Amine Ferrag, Leandros Maglaras, Abdelouahid Derhab, and Jorge B. Bernabe. Authentication and Authorization for Mobile IoT Devices Using Biofeatures: Recent Advances and Future Trends. Security and Communication Networks, 2019.
  5. Nethanel Gelernter, Senia Kalma, Bar Magnezi, and Hen Porcilan. The Password Reset MitM Attack. Proceedings - IEEE Symposium on Security and Privacy, 2017.
  6. Xin Huang, Yang Jiang, Xiong Gao, Rong Fu, and Tingting Zhang. Sensor aided authentication. Communications in Computer and Information Science, 76 CCIS:265–277, 2010.
  7. Suraiya Jabin, Sarmod Singh, Akshay Arora, Farhana Javed Zareen, Chirag Matta, Akshay Arora, Sarmod Singh, Suraiya Jabin, Sarmod Singh, Akshay Arora, Farhana Javed Zareen, and Chirag Matta. An authentication system using keystroke dynamics. International Journal of Biometrics, 10(1):65–76, 2018.
  8. D. Jagadiswary and D. Saraswady. Biometric Authentication Using Fused Multimodal Biometric. Procedia Computer Science, 2016.
  9. Rajesh Kumar, Partha Pratim Kundu, and Vir V Phoha. Continuous authentication using one-class classifiers and their fusion. 2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis, ISBA 2018, 2018.
  10. Xuelong Li, Stephen J. Maybank, Shuicheng Yan, Dacheng Tao, and Dong Xu. Gait components and their application to gender recognition, mar 2008.
  11. Ximing Liu. When Human Cognitive Modeling Meets PINs: User-Independent Inter-Keystroke Timing Attacks. PhD thesis, Singapore Management University, 2019.
  12. Hai Rong Lv, Zhong Lin Lin, Wen Jun Yin, and Jin Dong. Emotion recognition based on pressure sensor keyboards. In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME 2008), pages 1089–1092, 2008.
  13. J K Mohsin, Liangxiu Han, Mohammad Hammoudeh, and Rob Hegarty. Two factor vs multi-factor, an authentication battle in mobile cloud computing environments. ACM International Conference Proceeding Series, 2017.
  14. J R Montalvao Filho and E O Freire. Multimodal biometric fusion—joint typist (keystroke) and speaker verification. Telecommunications Symposium, 2006 International, pages 609–614, 2006.
  15. Aswathy Parappuram, T. R. Nidhina, and Greeshma N. Gopal. Continuous user identity verification using typing error classifications. Proceedings of the IEEE International Conference on Computing, Communication and Automation (ICCCA 2016), 2017.
  16. Vishal M. Patel, Raghuraman Gopalan, Ruonan Li, and Rama Chellappa. Visual Domain Adaptation: A survey of recent advances. IEEE Signal Processing Magazine, 32(3), 2015.
  17. Max Smith-Creasey and Muttukrishnan Rajarajan. Adaptive threshold scheme for touchscreen gesture continuous authentication using sensor trust. Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, 2017.
  18. Mary Villani, Charles Tappert, Giang Ngo, Justin Simone, Huguens St. Fort, and Sung Hyuk Cha. Keystroke biometric recognition studies on long-text input under ideal and application-oriented conditions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, 2006.
  19. Changsheng Wu, Wenbo Ding, Ruiyuan Liu, Jiyu Jie Wang, Aurelia C. Wang, Jiyu Jie Wang, Shengming Li, Yunlong Zi, and Zhong Lin Wang. Keystroke dynamics enabled authentication and identification using triboelectric nanogenerator array. Materials Today, 21(3):216–222, 2018.
Index Terms

Computer Science
Information Sciences
Biometrics
Human Gait
Keystroke Dynamics

Keywords

Keystroke Dynamics Gait Analysis Biometric Authentication Sensor Technology Multimodal Systems Equal Error Rate