activity
20162020
collaborators

5 papers

cs.CV2020

iLGaCo: Incremental Learning of Gait Covariate Factors

Zihao Mu, Francisco M. Castro, Manuel J. Marin-Jimenez +3

Gait is a popular biometric pattern used for identifying people based on their way of walking. Traditionally, gait recognition approaches based on deep learning are trained using t…

cs.CV2018

Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs

Francisco M. Castro, Nicolás Guil, Manuel J. Marín-Jiménez +2

Deep Learning (DL) applications are gaining momentum in the realm of Artificial Intelligence, particularly after GPUs have demonstrated remarkable skills for accelerating their cha…

cs.CV2018

End-to-End Incremental Learning

Francisco M. Castro, Manuel J. Marín-Jiménez, Nicolás Guil +2

Although deep learning approaches have stood out in recent years due to their state-of-the-art results, they continue to suffer from catastrophic forgetting, a dramatic decrease in…

cs.CV2018

Multimodal feature fusion for CNN-based gait recognition: an empirical comparison

Francisco Manuel Castro, Manuel Jesús Marín-Jiménez, Nicolás Guil +1

People identification in video based on the way they walk (i.e. gait) is a relevant task in computer vision using a non-invasive approach. Standard and current approaches typically…

cs.CV2016

Automatic learning of gait signatures for people identification

F. M. Castro, M. J. Marin-Jimenez, N. Guil +1

This work targets people identification in video based on the way they walk (i.e. gait). While classical methods typically derive gait signatures from sequences of binary silhouett…