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Incremental Learning from Low-labelled Stream Data in Open-Set Video Face Recognition
dc.contributor.author | López-López, Eric | |
dc.contributor.author | Pardo, Xosé Manuel | |
dc.contributor.author | Regueiro, Carlos V. | |
dc.date.accessioned | 2022-09-06T15:09:34Z | |
dc.date.available | 2022-09-06T15:09:34Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | LOPEZ-LOPEZ, Eric, PARDO, Xose M. and REGUEIRO, Carlos V., 2022. Incremental Learning from Low-labelled Stream Data in Open-Set Video Face Recognition. Pattern Recognition. 1 November 2022. Vol. 131, p. 108885. DOI 10.1016/j.patcog.2022.108885 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/31524 | |
dc.description.abstract | [Abstract] Deep Learning approaches have brought solutions, with impressive performance, to general classification problems where wealthy of annotated data are provided for training. In contrast, less progress has been made in continual learning of a set of non-stationary classes, mainly when applied to unsupervised problems with streaming data. Here, we propose a novel incremental learning approach which combines a deep features encoder with an Open-Set Dynamic Ensembles of SVM, to tackle the problem of identifying individuals of interest (IoI) from streaming face data. From a simple weak classifier trained on a few video-frames, our method can use unsupervised operational data to enhance recognition. Our approach adapts to new patterns avoiding catastrophic forgetting and partially heals itself from miss-adaptation. Besides, to better comply with real world conditions, the system was designed to operate in an open-set setting. Results show a benefit of up to 15% F1-score increase respect to non-adaptive state-of-the-art methods. | es_ES |
dc.description.sponsorship | This work has received financial support from the Spanish government (project PID2020-119367RB-I00); from the Xunta de Galicia, Consellaría de Cultura, Educación e Ordenación Universitaria (accreditations 2019-2022 ED431G-2019/04 and ED431G 2019/01, and reference competitive groups 2021-2024 ED431C 2021/48 and ED431C 2021/30), and from the European Regional Development Fund (ERDF). Eric López-López has received financial support from the Xunta de Galicia and the European Union (European Social Fund - ESF) | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G-2019/04 | es_ES |
dc.description.sponsorship | Xunta de Galicia; and ED431G 2019/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2021/48 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2021/30 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.patcog.2022.108885 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Open-set face recognition | es_ES |
dc.subject | Incremental Learning | es_ES |
dc.subject | Self-updating | es_ES |
dc.subject | Adaptive biometrics | es_ES |
dc.subject | Video-surveillance | es_ES |
dc.title | Incremental Learning from Low-labelled Stream Data in Open-Set Video Face Recognition | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Pattern Recognition | es_ES |
UDC.volume | 131 | es_ES |
UDC.startPage | 108885 | es_ES |
dc.identifier.doi | 10.1016/j.patcog.2022.108885 |
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