RT Conference Proceedings
T1 Multimodal Human Pose Feature Fusion for Gait Recognition.
A1 Cubero, Nicolás
A1 Castro Payán, Francisco Manuel
A1 Ramos-Cózar, Julián
A1 Guil-Mata, Nicolás
A1 Marín Jiménez, Manuel Jesús
K1 Biometría
K1 Reconocimiento óptico de formas (Informática)
AB Gait recognition allows identifying people at a distance based on the way they walk (i.e. gait) in a non-invasive approach. Most of the approaches published in the last decades are dominated by the use of silhouettes or other appearance-based modalities to describe the Gait cycle. In an attempt to exclude the appearance data, many works have been published that address the use of the human pose as a modality to describe the walking movement. However, as the pose contains less information when used as a single modality, the performance achieved by the models is generally poorer. To overcome such limitations, we propose a multimodal setup that combines multiple pose representation models. To this end, we evaluate multiple fusion strategies to aggregate the features derived from each pose modality at every model stage. Moreover, we introduce a weighted sum with trainable weights that can adaptively learn the optimal balance among pose modalities. Our experimental results show that (a) our fusion strategies can effectively combine different pose modalities by improving their baseline performance; and, (b) by using only human pose, our approach outperforms most of the silhouette-based state-of-the-art approaches. Concretely, we obtain 92.8% mean Top-1 accuracy in CASIA-B.
YR 2023
FD 2023
LK https://hdl.handle.net/10630/27271
UL https://hdl.handle.net/10630/27271
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026