Recognition of Human Gait Based on Ground Reaction Forces and Combined Data From Two Gait Laboratories

1 Yang W, Wang S, Hu J, Zheng G, Valli C. Security and accuracy of fingerprint-based biometrics: A review. Symmetry (Basel). 2019; 11(2): 141. https://doi.org/10.3390/sym11020141 Search in Google Scholar

2 Lohr D, Komogortsev OV. Eye Know You Too: Toward Viable Endto-End Eye Movement Biometrics for User Authentication. IEEE Transactions on Information Forensics and Security. 2022;17:3151–64. https://doi.org/10.1109/TIFS.2022.3201369 Search in Google Scholar

3 Chen X, Li Z, Setlur S, Xu W. Exploring racial and gender disparities in voice biometrics. Scientific Reports. 2022; 12(1), 3723. https://doi.org/10.1038/s41598-022-06673-y Search in Google Scholar

4 Stragapede G, Delgado-Santos P, Tolosana R, Vera-Rodriguez R, Guest R, Morales A. Mobile keystroke biometrics using transformers. In 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG). IEEE. Waikoloa Beach, HI, USA 2023. 1-6. https://doi.org/10.1109/FG57933.2023.10042710 Search in Google Scholar

5 Taskiran M, Kahraman N, Erdem CE. Face recognition: Past, present and future (a review). Digital Signal Processing. 2020; 106: 102809. https://doi.org/10.1016/j.dsp.2020.102809 Search in Google Scholar

6 Parashar A, Parashar A, Ding W, Shekhawat RS, Rida I. Deep learning pipelines for recognition of gait biometrics with covariates: A comprehensive review. Artificial Intelligence Review. 2023; 1-65. https://doi.org/10.1007/s10462-022-10365-4 Search in Google Scholar

7 Szczuko P, Harasimiuk A, Czyżewski A. Evaluation of decision fusion methods for multimodal biometrics in the banking application. Sensors. 2022; 22(6): 2356. https://doi.org/10.3390/s22062356 Search in Google Scholar

8 Ren H, Sun L, Guo J, Han C. A dataset and benchmark for multi-modal biometric recognition based on fingerprint and finger vein. IEEE Transactions on Information Forensics and Security. 2022; 17: 2030-2043. https://doi.org/10.1109/TIFS.2022.3175599 Search in Google Scholar

9 Delgado-Santos P, Tolosana R, Guest R, Deravi F, Vera-Rodriguez R. Exploring transformers for behavioural biometrics: A case study in gait recognition. Pattern Recognition. 2023; 143: 109798. https://doi.org/10.1016/j.patcog.2023.109798 Search in Google Scholar

10 Rani V, Kumar M. Human gait recognition: A systematic review. Multimedia Tools and Applications. 2023; 1-35. https://doi.org/10.1007/s11042-023-15079-5 Search in Google Scholar

11 Horst F, Slijepcevic D, Simak M, Schöllhorn WI. Gutenberg Gait Database, a ground reaction force database of level overground walking in healthy individuals. Scientific data. 2021; 8(1): 232. https://doi.org/10.1038/s41597-021-01014-6 Search in Google Scholar

12 Derlatka M, Parfieniuk M. Real-world measurements of ground reaction forces of normal gait of young adults wearing various foot-wear. Scientific data. 2023; 10(1): 60. https://doi.org/10.1038/s41597-023-01964-z Search in Google Scholar

13 Makihara Y, Nixon MS, Yagi Y. Gait recognition: Databases, representations, and applications. Computer Vision: A Reference Guide. 2020; 1-13. https://doi.org/10.1007/978-3-030-03243-2_883-1 Search in Google Scholar

14 Song C, Huang Y, Wang W, Wang L. CASIA-E: a large comprehensive dataset for gait recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2022; 45(3): 2801-2815. https://doi.org/10.1109/TPAMI.2022.3183288 Search in Google Scholar

15 Ngo TT, Ahad MAR, Antar AD, Ahmed M, Muramatsu D, Makihara Y, et al. OU-ISIR wearable sensor-based gait challenge: Age and gender. In 2019 International Conference on Biometrics (ICB). 2019; 1-6. IEEE. https://doi.org/10.1109/ICB45273.2019.8987235 Search in Google Scholar

16 Malekzadeh M, Clegg RG, Cavallaro A, Haddadi H. Protecting sensory data against sensitive inferences. In Proceedings of the 1st Workshop on Privacy by Design in Distributed Systems. 2018; 1-6. https://doi.org/10.1145/3195258.3195260 Search in Google Scholar

17 Zou Q, Wang Y, Wang Q, Zhao Y, Li Q. Deep learning-based gait recognition using smartphones in the wild. IEEE Transactions on Information Forensics and Security. 2020; 15: 3197-3212. https://doi.org/10.1109/TIFS.2020.2985628 Search in Google Scholar

18 Tan D, Huang K, Yu S, Tan T. (2006, August). Efficient night gait recognition based on template matching. In 18th International Conference on Pattern Recognition (ICPR'06). IEEE. 2006; 3: 1000-1003. https://doi.org/10.1109/ICPR.2006.478 Search in Google Scholar

19 Yu S, Tan D, Tan T. A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In 18th International Conference on Pattern Recognition (ICPR'06). 2006; 4: 441-444. IEEE. https://doi.org/10.1109/ICPR.2006.67 Search in Google Scholar

20 Sarkar S, Phillips PJ, Liu Z, Vega IR, Grother P, Bowyer KW. The humanID gait challenge problem: Data sets, performance, and analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2005; 27(2): 162-177. https://doi.org/10.1109/TPAMI.2005.39 Search in Google Scholar

21 Smith T, Ditroilo M. Force plate coverings significantly affect measurement of ground reaction forces. Plos one. 2023; 18(11): e0293959. https://doi.org/10.1371/journal.pone.0293959 Search in Google Scholar

22 Horst F, Slijepcevic D, Simak M, Horsak B, Schöllhorn WI, Zeppelzauer M. Modeling Biological Individuality Using Machine Learning: A Study on Human Gait. Computational and Structural Biotechnology Journal. 2023; 21:3414-3423 https://doi.org/10.1016/j.csbj.2023.06.009 Search in Google Scholar

23 Derlatka M, Borowska M. Ensemble of heterogeneous base classifiers for human gait recognition. Sensors, 2023; 23(1): 508. https://doi.org/10.3390/s23010508 Search in Google Scholar

24 Guo Y, Hastie T, Tibshirani R. Regularized linear discriminant analysis and its application in microarrays. Biostatistics. 2007; 8:86–100. https://doi.org/10.1093/biostatistics/kxj035. Search in Google Scholar

25 Kingma DP, Ba J. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. 2014. doi:10.48550/arXiv.1412.6980 Search in Google Scholar

26 Derlatka M, Bogdan M. Recognition of a person wearing sport shoes or high heels through gait using two types of sensors. Sensors. 2018; 18(5): 1639. https://doi.org/10.3390/s18051639 Search in Google Scholar

27 Duncanson K, Thwaites S, Booth D, Abbasnejad E, Robertson WS, Thewlis D. The most discriminant components of force platform data for gait based person re-identification. 2021. https://doi.org/10.36227/techrxiv,16683229, v1 Search in Google Scholar