Matches in SemOpenAlex for { <https://semopenalex.org/work/W2770241112> ?p ?o ?g. }
Showing items 1 to 62 of
62
with 100 items per page.
- W2770241112 abstract "Nowadays, deep learning is a technique that takes place in many computer vision related applications and studies. While it is put in the practice mostly on content based image retrieval, there is still room for improvement by employing it in diverse computer vision applications. In this study, we aimed to build a Convolutional Neural Network (CNN) based Facial Expression Recognition System (FER), in order to automatically classify expressions presented in Facial Expression Recognition (FER2013) database. Our presented CNN achieved % 57.1 success rate on FER2013 database." @default.
- W2770241112 created "2017-12-04" @default.
- W2770241112 creator A5000741407 @default.
- W2770241112 creator A5039647395 @default.
- W2770241112 creator A5070035016 @default.
- W2770241112 date "2017-09-01" @default.
- W2770241112 modified "2023-10-05" @default.
- W2770241112 title "Facial emotion recognition on a dataset using convolutional neural network" @default.
- W2770241112 cites W1970088388 @default.
- W2770241112 cites W2041616772 @default.
- W2770241112 cites W2253728219 @default.
- W2770241112 cites W2556844902 @default.
- W2770241112 doi "https://doi.org/10.1109/idap.2017.8090281" @default.
- W2770241112 hasPublicationYear "2017" @default.
- W2770241112 type Work @default.
- W2770241112 sameAs 2770241112 @default.
- W2770241112 citedByCount "38" @default.
- W2770241112 countsByYear W27702411122018 @default.
- W2770241112 countsByYear W27702411122019 @default.
- W2770241112 countsByYear W27702411122020 @default.
- W2770241112 countsByYear W27702411122021 @default.
- W2770241112 countsByYear W27702411122022 @default.
- W2770241112 countsByYear W27702411122023 @default.
- W2770241112 crossrefType "proceedings-article" @default.
- W2770241112 hasAuthorship W2770241112A5000741407 @default.
- W2770241112 hasAuthorship W2770241112A5039647395 @default.
- W2770241112 hasAuthorship W2770241112A5070035016 @default.
- W2770241112 hasConcept C108583219 @default.
- W2770241112 hasConcept C153180895 @default.
- W2770241112 hasConcept C154945302 @default.
- W2770241112 hasConcept C195704467 @default.
- W2770241112 hasConcept C2987714656 @default.
- W2770241112 hasConcept C31510193 @default.
- W2770241112 hasConcept C31972630 @default.
- W2770241112 hasConcept C41008148 @default.
- W2770241112 hasConcept C81363708 @default.
- W2770241112 hasConceptScore W2770241112C108583219 @default.
- W2770241112 hasConceptScore W2770241112C153180895 @default.
- W2770241112 hasConceptScore W2770241112C154945302 @default.
- W2770241112 hasConceptScore W2770241112C195704467 @default.
- W2770241112 hasConceptScore W2770241112C2987714656 @default.
- W2770241112 hasConceptScore W2770241112C31510193 @default.
- W2770241112 hasConceptScore W2770241112C31972630 @default.
- W2770241112 hasConceptScore W2770241112C41008148 @default.
- W2770241112 hasConceptScore W2770241112C81363708 @default.
- W2770241112 hasLocation W27702411121 @default.
- W2770241112 hasOpenAccess W2770241112 @default.
- W2770241112 hasPrimaryLocation W27702411121 @default.
- W2770241112 hasRelatedWork W2001423728 @default.
- W2770241112 hasRelatedWork W2134472250 @default.
- W2770241112 hasRelatedWork W2175746458 @default.
- W2770241112 hasRelatedWork W2486556835 @default.
- W2770241112 hasRelatedWork W2613736958 @default.
- W2770241112 hasRelatedWork W2732542196 @default.
- W2770241112 hasRelatedWork W2765676680 @default.
- W2770241112 hasRelatedWork W2773840829 @default.
- W2770241112 hasRelatedWork W2977314777 @default.
- W2770241112 hasRelatedWork W3034241453 @default.
- W2770241112 isParatext "false" @default.
- W2770241112 isRetracted "false" @default.
- W2770241112 magId "2770241112" @default.
- W2770241112 workType "article" @default.