2026-05-30T01:49:47Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/145922026-02-03T11:56:45Zcom_10630_2254col_10630_37959

Evaluation of CNN architectures for gait recognition based on optical flow maps Castro, Francisco M. Marín-Jiménez, Manuel J. Guil-Mata, Nicolás López-Tapia, S. Pérez de la Blanca, N. Identificación This work targets people identification in video based on the way they walk (\ie gait) by using deep learning architectures. We explore the use of convolutional neural networks (CNN) for learning high-level descriptors from low-level motion features (\ie optical flow components). The low number of training samples for each subject and the use of a test set containing subjects different from the training ones makes the search of a good CNN architecture a challenging task. 2017-10-06T10:32:10Z 2017-10-06T10:32:10Z 2017-10-06T10:32:10Z 2017 conference output http://hdl.handle.net/10630/14592 http://orcid.org/orcid.org/0000-0003-3431-6516 eng BioSig 2017 Darmstadt, Germany 21 y 22 de septiembre de 2017 open access by-nc-nd