Shallow and Deep Learning in Footstep Recognition: A Survey
Iskandar, Ayman; Alfonse, Marco; Roushdy M.; El-Sayed M. El-Horbaty;
Abstract
Analyzing gait data is a branch of biomechanics that offers a degree of privacy, low-cost, and effortless objective identification for individuals. Consequently, gait recognition can be used as a replacement for passwords, or as an extra security measure with existing passwords. This paper focuses on surveying footstep recognition, comparing deep learning and shallow learning, and providing an overview of the current state of footstep recognition. It might be useful to both professionals and beginners in this field of research.
Other data
| Title | Shallow and Deep Learning in Footstep Recognition: A Survey | Authors | Iskandar, Ayman; Alfonse, Marco ; Roushdy M. ; El-Sayed M. El-Horbaty | Keywords | Biometrics;Deep learning;Floor sensor;Gait;Neural network | Issue Date | 1-Jan-2022 | Publisher | IEEE | Conference | 5th International Conference on Computing and Informatics, ICCI 2022 | ISBN | 9781665499729 | DOI | 10.1109/ICCI54321.2022.9756118 | Scopus ID | 2-s2.0-85129359468 | Web of science ID | WOS:000812327000055 |
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