Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387682242> ?p ?o ?g. }
Showing items 1 to 71 of
71
with 100 items per page.
- W4387682242 endingPage "1" @default.
- W4387682242 startingPage "1" @default.
- W4387682242 abstract "Micro-expression recognition has gained much attention in research communities. Among its proposed solutions, deep learning approaches have shown promising results over the past few years. In this paper, we propose a multi-stream deep convolution neural network with ensemble classification for facial micro-expression recognition. The multi-stream network uses the deep features of a residual network, densely connected convolutional network, and visual geometry group. The features of these aforementioned architectures are extracted from their pooling layers and become very resource-intensive due to their high dimensions. The principal component analysis is applied to these features for their dimensionality reduction. Stacking, an ensemble classification technique, is performed on these deep features with three base learners (random tree, J48, random forest) and a meta learner (random forest). Experiments were performed using publicly available datasets, namely: CASME-II, CASME2, SMIC, and SAMM. The proposed approach (PA) is compared with twelve approaches. The results show that the PA outperformed the existing approaches in terms of accuracy and time efficiency." @default.
- W4387682242 created "2023-10-17" @default.
- W4387682242 creator A5012979408 @default.
- W4387682242 creator A5046199065 @default.
- W4387682242 creator A5054415369 @default.
- W4387682242 creator A5067387339 @default.
- W4387682242 creator A5069205023 @default.
- W4387682242 creator A5072872218 @default.
- W4387682242 creator A5080581779 @default.
- W4387682242 date "2023-01-01" @default.
- W4387682242 modified "2023-10-17" @default.
- W4387682242 title "Multi-stream Deep Convolution Neural Network with Ensemble Learning for Facial Micro-expression Recognition" @default.
- W4387682242 doi "https://doi.org/10.1109/access.2023.3325108" @default.
- W4387682242 hasPublicationYear "2023" @default.
- W4387682242 type Work @default.
- W4387682242 citedByCount "0" @default.
- W4387682242 crossrefType "journal-article" @default.
- W4387682242 hasAuthorship W4387682242A5012979408 @default.
- W4387682242 hasAuthorship W4387682242A5046199065 @default.
- W4387682242 hasAuthorship W4387682242A5054415369 @default.
- W4387682242 hasAuthorship W4387682242A5067387339 @default.
- W4387682242 hasAuthorship W4387682242A5069205023 @default.
- W4387682242 hasAuthorship W4387682242A5072872218 @default.
- W4387682242 hasAuthorship W4387682242A5080581779 @default.
- W4387682242 hasBestOaLocation W43876822421 @default.
- W4387682242 hasConcept C108583219 @default.
- W4387682242 hasConcept C119857082 @default.
- W4387682242 hasConcept C12267149 @default.
- W4387682242 hasConcept C153180895 @default.
- W4387682242 hasConcept C154945302 @default.
- W4387682242 hasConcept C169258074 @default.
- W4387682242 hasConcept C41008148 @default.
- W4387682242 hasConcept C45347329 @default.
- W4387682242 hasConcept C45942800 @default.
- W4387682242 hasConcept C50644808 @default.
- W4387682242 hasConcept C52001869 @default.
- W4387682242 hasConcept C52003472 @default.
- W4387682242 hasConcept C70437156 @default.
- W4387682242 hasConcept C81363708 @default.
- W4387682242 hasConceptScore W4387682242C108583219 @default.
- W4387682242 hasConceptScore W4387682242C119857082 @default.
- W4387682242 hasConceptScore W4387682242C12267149 @default.
- W4387682242 hasConceptScore W4387682242C153180895 @default.
- W4387682242 hasConceptScore W4387682242C154945302 @default.
- W4387682242 hasConceptScore W4387682242C169258074 @default.
- W4387682242 hasConceptScore W4387682242C41008148 @default.
- W4387682242 hasConceptScore W4387682242C45347329 @default.
- W4387682242 hasConceptScore W4387682242C45942800 @default.
- W4387682242 hasConceptScore W4387682242C50644808 @default.
- W4387682242 hasConceptScore W4387682242C52001869 @default.
- W4387682242 hasConceptScore W4387682242C52003472 @default.
- W4387682242 hasConceptScore W4387682242C70437156 @default.
- W4387682242 hasConceptScore W4387682242C81363708 @default.
- W4387682242 hasLocation W43876822421 @default.
- W4387682242 hasOpenAccess W4387682242 @default.
- W4387682242 hasPrimaryLocation W43876822421 @default.
- W4387682242 hasRelatedWork W2188759683 @default.
- W4387682242 hasRelatedWork W2944292463 @default.
- W4387682242 hasRelatedWork W2953079191 @default.
- W4387682242 hasRelatedWork W3014252901 @default.
- W4387682242 hasRelatedWork W3110815158 @default.
- W4387682242 hasRelatedWork W3208169454 @default.
- W4387682242 hasRelatedWork W38301456 @default.
- W4387682242 hasRelatedWork W4317376680 @default.
- W4387682242 hasRelatedWork W4360777922 @default.
- W4387682242 hasRelatedWork W4386883672 @default.
- W4387682242 isParatext "false" @default.
- W4387682242 isRetracted "false" @default.
- W4387682242 workType "article" @default.