Matches in SemOpenAlex for { <https://semopenalex.org/work/W4383669992> ?p ?o ?g. }
Showing items 1 to 75 of
75
with 100 items per page.
- W4383669992 endingPage "186" @default.
- W4383669992 startingPage "173" @default.
- W4383669992 abstract "Emotions don’t have correct definition and are abstract in nature. Though emotions are easily understandable, it is hard to process and know the expression within fraction of second. Therefore, machine learning algorithms and methods help recognizing the correct expressions especially the microexpressions. These facial expression recognition models can be categorized under the supervised learning where a model is already trained and remaining data (images/videos) can be processed to get the expressions. We have used CNN and FER (facial expression recognition) to identify the expressions, microexpressions and their correlation in the given images or video. It helps in developing a model that accurately predicts the expressions on the face. Basic emotions like anger, sadness, happiness, neutral, surprise and disgust are considered to categorize the images based on the scores obtained by executing our proposed model. There are few layers in our proposed model like CNN layer, dropout layer, etc. We strongly believe with the help of this analytic approach one can easily identify the emotions/microexpression either using a web camera or processing a video file. In the field of robotics application, computer vision these machine learning-based models will bring the revolution. Multi-cascade CNN and HAAR cascade classifier helps to processing the streaming data and identifying the expression and their relations for the images." @default.
- W4383669992 created "2023-07-09" @default.
- W4383669992 creator A5005820246 @default.
- W4383669992 creator A5078110452 @default.
- W4383669992 date "2023-01-01" @default.
- W4383669992 modified "2023-09-25" @default.
- W4383669992 title "Microexpression Analysis Using a Model Based on CNN and Facial Expression Recognition" @default.
- W4383669992 cites W2911463544 @default.
- W4383669992 cites W2916690307 @default.
- W4383669992 cites W2944537106 @default.
- W4383669992 cites W2950753428 @default.
- W4383669992 cites W2966935339 @default.
- W4383669992 cites W3003981334 @default.
- W4383669992 cites W3020496054 @default.
- W4383669992 cites W3033437480 @default.
- W4383669992 cites W3036975059 @default.
- W4383669992 cites W3047499479 @default.
- W4383669992 cites W3082834267 @default.
- W4383669992 cites W3091937440 @default.
- W4383669992 cites W3114081512 @default.
- W4383669992 cites W3120208420 @default.
- W4383669992 cites W3126576784 @default.
- W4383669992 cites W3160847796 @default.
- W4383669992 cites W3168389866 @default.
- W4383669992 doi "https://doi.org/10.1007/978-981-99-0483-9_16" @default.
- W4383669992 hasPublicationYear "2023" @default.
- W4383669992 type Work @default.
- W4383669992 citedByCount "0" @default.
- W4383669992 crossrefType "book-chapter" @default.
- W4383669992 hasAuthorship W4383669992A5005820246 @default.
- W4383669992 hasAuthorship W4383669992A5078110452 @default.
- W4383669992 hasConcept C118552586 @default.
- W4383669992 hasConcept C119857082 @default.
- W4383669992 hasConcept C153180895 @default.
- W4383669992 hasConcept C154945302 @default.
- W4383669992 hasConcept C15744967 @default.
- W4383669992 hasConcept C195704467 @default.
- W4383669992 hasConcept C199360897 @default.
- W4383669992 hasConcept C206310091 @default.
- W4383669992 hasConcept C2777375102 @default.
- W4383669992 hasConcept C2779302386 @default.
- W4383669992 hasConcept C2779812673 @default.
- W4383669992 hasConcept C41008148 @default.
- W4383669992 hasConcept C90559484 @default.
- W4383669992 hasConceptScore W4383669992C118552586 @default.
- W4383669992 hasConceptScore W4383669992C119857082 @default.
- W4383669992 hasConceptScore W4383669992C153180895 @default.
- W4383669992 hasConceptScore W4383669992C154945302 @default.
- W4383669992 hasConceptScore W4383669992C15744967 @default.
- W4383669992 hasConceptScore W4383669992C195704467 @default.
- W4383669992 hasConceptScore W4383669992C199360897 @default.
- W4383669992 hasConceptScore W4383669992C206310091 @default.
- W4383669992 hasConceptScore W4383669992C2777375102 @default.
- W4383669992 hasConceptScore W4383669992C2779302386 @default.
- W4383669992 hasConceptScore W4383669992C2779812673 @default.
- W4383669992 hasConceptScore W4383669992C41008148 @default.
- W4383669992 hasConceptScore W4383669992C90559484 @default.
- W4383669992 hasLocation W43836699921 @default.
- W4383669992 hasOpenAccess W4383669992 @default.
- W4383669992 hasPrimaryLocation W43836699921 @default.
- W4383669992 hasRelatedWork W1974787498 @default.
- W4383669992 hasRelatedWork W2028755160 @default.
- W4383669992 hasRelatedWork W2062652500 @default.
- W4383669992 hasRelatedWork W2119012436 @default.
- W4383669992 hasRelatedWork W2140157036 @default.
- W4383669992 hasRelatedWork W2810172795 @default.
- W4383669992 hasRelatedWork W2947045439 @default.
- W4383669992 hasRelatedWork W4214523017 @default.
- W4383669992 hasRelatedWork W4285343266 @default.
- W4383669992 hasRelatedWork W2519456985 @default.
- W4383669992 isParatext "false" @default.
- W4383669992 isRetracted "false" @default.
- W4383669992 workType "book-chapter" @default.