A Deep Learning Approach for Real-Time Analysis of Attendees’ Engagement in Public Events
Smart city analytics requires the harnessing and analysis of emotions and sentiments conveyed by images and video footage. In recent years, facial sentiment analysis attracted significant attention for different application areas, including marketing, gaming, political analytics, healthcare, and hum...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2021
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://bspace.buid.ac.ae/handle/1234/3075 https://doi.org/10.24138/jcomss-2021-0072. |
| الوسوم: |
إضافة وسم
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| _version_ | 1862980615032274944 |
|---|---|
| author | Samuel Mathew, Sujith |
| author2 | Alkhatib, Manar El Barachi , May |
| author2_role | author author |
| author_facet | Samuel Mathew, Sujith Alkhatib, Manar El Barachi , May |
| author_role | author |
| dc.creator.none.fl_str_mv | Samuel Mathew, Sujith Alkhatib, Manar El Barachi , May |
| dc.date.none.fl_str_mv | 2021-06-02 2025-05-15T11:19:16Z 2025-05-15T11:19:16Z |
| dc.identifier.none.fl_str_mv | Sujith Samuel Mathew, Manar AlKhatib and May El Barachi (2021) “A Deep Learning Approach for Real-Time Analysis of Attendees’ Engagement in Public Events,” Journal of Communications Software and Systems, 17(2), pp. 106–115. 1845-6421, 1846-6079 https://bspace.buid.ac.ae/handle/1234/3075 https://doi.org/10.24138/jcomss-2021-0072. |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | CCIS |
| dc.relation.none.fl_str_mv | Journal of Communications Software and Systemsv17 n2 (20210601): 106-115 |
| dc.subject.none.fl_str_mv | smart cities, sentiment analysis, facial recognition, convolutional neural networks, deep learning |
| dc.title.none.fl_str_mv | A Deep Learning Approach for Real-Time Analysis of Attendees’ Engagement in Public Events |
| dc.type.none.fl_str_mv | Article |
| description | Smart city analytics requires the harnessing and analysis of emotions and sentiments conveyed by images and video footage. In recent years, facial sentiment analysis attracted significant attention for different application areas, including marketing, gaming, political analytics, healthcare, and human computer interaction. Aiming at contributing to this area, we propose a deep learning model enabling the accurate emotion analysis of crowded scenes containing complete and partially occluded faces, with different angles, various distances from the camera, and varying resolutions. Our model consists of a sophisticated convolutional neural network (CNN) that is combined with pooling, densifying, flattening, and Softmax layers to achieve accurate sentiment and emotion analysis of facial images. The proposed model was successfully tested using 3,750 images containing 22,563 faces, collected from a large consumer electronics trade show. The model was able to correctly classify the test images which contained faces with different angles, distances, occlusion areas, facial orientation and resolutions. It achieved an average accuracy of 90.6% when distinguishing between seven emotions (Happiness, smiling, laughter, neutral, sadness, anger, and surprise) in complete faces, and 86.16% accuracy in partially occluded faces. Such model can be leveraged for the automatic analysis of attendees’ engagement level in events. Furthermore, it can open the door for many useful applications in smart cities, such as measuring employees’ satisfaction and citizens’ happiness. |
| id | budr_901caec5b05e1b709decd27a204eb57c |
| identifier_str_mv | Sujith Samuel Mathew, Manar AlKhatib and May El Barachi (2021) “A Deep Learning Approach for Real-Time Analysis of Attendees’ Engagement in Public Events,” Journal of Communications Software and Systems, 17(2), pp. 106–115. 1845-6421, 1846-6079 |
| language_invalid_str_mv | en_US |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/3075 |
| publishDate | 2021 |
| publisher.none.fl_str_mv | CCIS |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | A Deep Learning Approach for Real-Time Analysis of Attendees’ Engagement in Public EventsSamuel Mathew, SujithAlkhatib, ManarEl Barachi , Maysmart cities, sentiment analysis, facial recognition, convolutional neural networks, deep learningSmart city analytics requires the harnessing and analysis of emotions and sentiments conveyed by images and video footage. In recent years, facial sentiment analysis attracted significant attention for different application areas, including marketing, gaming, political analytics, healthcare, and human computer interaction. Aiming at contributing to this area, we propose a deep learning model enabling the accurate emotion analysis of crowded scenes containing complete and partially occluded faces, with different angles, various distances from the camera, and varying resolutions. Our model consists of a sophisticated convolutional neural network (CNN) that is combined with pooling, densifying, flattening, and Softmax layers to achieve accurate sentiment and emotion analysis of facial images. The proposed model was successfully tested using 3,750 images containing 22,563 faces, collected from a large consumer electronics trade show. The model was able to correctly classify the test images which contained faces with different angles, distances, occlusion areas, facial orientation and resolutions. It achieved an average accuracy of 90.6% when distinguishing between seven emotions (Happiness, smiling, laughter, neutral, sadness, anger, and surprise) in complete faces, and 86.16% accuracy in partially occluded faces. Such model can be leveraged for the automatic analysis of attendees’ engagement level in events. Furthermore, it can open the door for many useful applications in smart cities, such as measuring employees’ satisfaction and citizens’ happiness.CCIS2025-05-15T11:19:16Z2025-05-15T11:19:16Z2021-06-02ArticleSujith Samuel Mathew, Manar AlKhatib and May El Barachi (2021) “A Deep Learning Approach for Real-Time Analysis of Attendees’ Engagement in Public Events,” Journal of Communications Software and Systems, 17(2), pp. 106–115.1845-6421, 1846-6079https://bspace.buid.ac.ae/handle/1234/3075https://doi.org/10.24138/jcomss-2021-0072.en_USJournal of Communications Software and Systemsv17 n2 (20210601): 106-115oai:bspace.buid.ac.ae:1234/30752026-01-29T16:51:44Z |
| spellingShingle | A Deep Learning Approach for Real-Time Analysis of Attendees’ Engagement in Public Events Samuel Mathew, Sujith smart cities, sentiment analysis, facial recognition, convolutional neural networks, deep learning |
| title | A Deep Learning Approach for Real-Time Analysis of Attendees’ Engagement in Public Events |
| title_full | A Deep Learning Approach for Real-Time Analysis of Attendees’ Engagement in Public Events |
| title_fullStr | A Deep Learning Approach for Real-Time Analysis of Attendees’ Engagement in Public Events |
| title_full_unstemmed | A Deep Learning Approach for Real-Time Analysis of Attendees’ Engagement in Public Events |
| title_short | A Deep Learning Approach for Real-Time Analysis of Attendees’ Engagement in Public Events |
| title_sort | A Deep Learning Approach for Real-Time Analysis of Attendees’ Engagement in Public Events |
| topic | smart cities, sentiment analysis, facial recognition, convolutional neural networks, deep learning |
| url | https://bspace.buid.ac.ae/handle/1234/3075 https://doi.org/10.24138/jcomss-2021-0072. |