Advances in Gait-Based Identification A Systematic Review of Deep Learning Models Leveraging Computer Vision Techniques /
This book provides a systematic review of gait-based person identification, categorizing studies into deep-learning and non-deep-learning approaches while analyzing key datasets and performance metrics. It explores challenges such as covariant factors, e.g., viewing angles, clothing, and accessories...
Tallennettuna:
| Päätekijät: | , |
|---|---|
| Yhteisötekijä: | |
| Aineistotyyppi: | Elektroninen E-kirja |
| Kieli: | englanti |
| Julkaistu: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
|
| Painos: | 1st ed. 2025. |
| Sarja: | Studies in Systems, Decision and Control,
593 |
| Aiheet: | |
| Linkit: | https://doi.org/10.1007/978-3-031-89560-9 |
| Tagit: |
Lisää tagi
Ei tageja, Lisää ensimmäinen tagi!
|
| Yhteenveto: | This book provides a systematic review of gait-based person identification, categorizing studies into deep-learning and non-deep-learning approaches while analyzing key datasets and performance metrics. It explores challenges such as covariant factors, e.g., viewing angles, clothing, and accessories, and highlights advancements in real-world gait recognition systems. With a structured methodology and transparent review process, this work serves as a valuable reference for researchers and a foundation for future developments in biometric identification. |
|---|---|
| Ulkoasu: | 1 online resource (XIX, 96 p. 22 illus.). 18 illus. in color. |
| ISBN: | 9783031895609 |
| ISSN: | 2198-4190 ; |
| DOI: | 10.1007/978-3-031-89560-9 |