Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/82546
Registo completo
Campo DC | Valor | Idioma |
---|---|---|
dc.contributor.author | Cruz, Gonzalo de la | por |
dc.contributor.author | Lira, Madalena | por |
dc.contributor.author | Luaces, Oscar | por |
dc.contributor.author | Remeseiro, Beatriz | por |
dc.date.accessioned | 2023-02-07T10:22:59Z | - |
dc.date.issued | 2022-09-09 | - |
dc.identifier.citation | G. d. l. Cruz, M. Lira, O. Luaces and B. Remeseiro, "Eye-LRCN: A Long-Term Recurrent Convolutional Network for Eye Blink Completeness Detection," in IEEE Transactions on Neural Networks and Learning Systems, 2022, doi: 10.1109/TNNLS.2022.3202643. | - |
dc.identifier.issn | 2162-237X | por |
dc.identifier.uri | https://hdl.handle.net/1822/82546 | - |
dc.description.abstract | Computer vision syndrome causes vision problems and discomfort mainly due to dry eye. Several studies show that dry eye in computer users is caused by a reduction in the blink rate and an increase in the prevalence of incomplete blinks. In this context, this article introduces Eye-LRCN, a new eye blink detection method that also evaluates the completeness of the blink. The method is based on a long-term recurrent convolutional network (LRCN), which combines a convolutional neural network (CNN) for feature extraction with a bidirectional recurrent neural network that performs sequence learning and classifies the blinks. A Siamese architecture is used during CNN training to overcome the high-class imbalance present in blink detection and the limited amount of data available to train blink detection models. The method was evaluated on three different tasks: blink detection, blink completeness detection, and eye state detection. We report superior performance to the state-of-the-art methods in blink detection and blink completeness detection, and remarkable results in eye state detection. | por |
dc.description.sponsorship | This work was supported in part by the Portuguese Foundation for Science and Technology (FCT) through the framework of the Strategic Funding under Grant UIDB/04650/2020. The work of Beatriz Remeseiro and Oscar Luaces was supported in part by the Ministry of Science and Innovation, Spain, under Grant PID2019-109238GB-C21. | por |
dc.language.iso | eng | por |
dc.publisher | IEEE | - |
dc.rights | restrictedAccess | por |
dc.subject | Blink completeness detection | por |
dc.subject | Computer vision syndrome (CVS) | por |
dc.subject | Eye state detection | por |
dc.subject | Long-term recurrent convolutional networks (LRCNs) | por |
dc.subject | Siamese neural networks | por |
dc.subject | Task analysis | por |
dc.subject | Feature extraction | por |
dc.subject | Computer architecture | por |
dc.subject | Face recognition | por |
dc.subject | Support vector machines | por |
dc.subject | Eyelids | por |
dc.subject | Convolutional neural networks | por |
dc.title | Eye-LRCN: A long-term recurrent convolutional network for eye blink completeness detection | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9885029/ | - |
dc.identifier.doi | 10.1109/TNNLS.2022.3202643 | por |
dc.date.embargo | 10000-01-01 | - |
dc.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | por |
dc.subject.wos | Science & Technology | por |
sdum.journal | IEEE Transactions on Neural Networks and Learning Systems | por |
Aparece nas coleções: | CDF - OCV - Artigos/Papers (with refereeing) |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
Eye-LRCN_A_Long-Term_Recurrent_Convolutional_Network_for_Eye_Blink_Completeness_Detection.pdf Acesso restrito! | 641,46 kB | Adobe PDF | Ver/Abrir |