RT Journal Article
T1 Deep multi-task learning for gait-based biometrics.
A1 Marín Jiménez, Manuel Jesús
A1 Castro, Francisco M.
A1 Guil-Mata, Nicolás
A1 de la Torre, Fernando
A1 Medina-Carnicer, Rafael
K1 Aprendizaje automático
AB The task of identifying people by the way they walk is known as `gait recognition'. Although gait is mainly used for identification, additional tasks as gender recognition or age estimation may be addressed based on gait as well. In such cases, traditional approaches consider those tasks as independent ones, defining separated task-specific features and models for them. This paper shows that by training jointly more than one gait-based tasks, the identification task converges faster than when it is trained independently, and the recognition performance of multi-task models is equal or superior to more complex single-task ones. Our model is a multi-task CNN that receives as input a fixed-length sequence of optical flow channels and outputs several biometric features (identity, gender and age).
PB IEEE
YR 2017
FD 2017
LK https://hdl.handle.net/10630/37143
UL https://hdl.handle.net/10630/37143
LA eng
NO https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/#preprint
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 27 mar 2026