Advances in Gait-Based Identification
A Systematic Review of Deep Learning Models Leveraging Computer Vision Techniques
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Beschreibung
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.
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.
Gives comprehensive, updated, and replicable systematic review Presents thorough analysis of methodologies, datasets, and results Provides an up-to-date, transparent, and comprehensive review of deep learning and non-deep learning models
Autor*in
Diogo R. M. Bastos
Themen in »Advances in Gait-Based Identification«
Gait-based identification Deep learning for gait recognition Computer vision in biometrics Gait identification review Biometric gait recognition