Affective computing model for natural interaction based on large-scale self-built dataset
Article Highlights 1. In this paper, we describe a way to make a new face expression dataset by putting together open source and self-collected datasets. 2. This allows us to create the largest face expression dataset in the industry, which is better in quality and has more data. 3. When we use this...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Springer
2023-01-01
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| Series: | SN Applied Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s42452-023-05277-z |
| _version_ | 1797952446918557696 |
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| author | Jin Lu Xiaoting Wan |
| author_facet | Jin Lu Xiaoting Wan |
| author_sort | Jin Lu |
| collection | DOAJ |
| description | Article Highlights 1. In this paper, we describe a way to make a new face expression dataset by putting together open source and self-collected datasets. 2. This allows us to create the largest face expression dataset in the industry, which is better in quality and has more data. 3. When we use this new dataset to train a face expression recognition model, it performs the best in the current industry. |
| first_indexed | 2024-04-10T22:46:27Z |
| format | Article |
| id | doaj.art-d2758870edd14c238a4e1a6820da4a64 |
| institution | Directory Open Access Journal |
| issn | 2523-3963 2523-3971 |
| language | English |
| last_indexed | 2024-04-10T22:46:27Z |
| publishDate | 2023-01-01 |
| publisher | Springer |
| record_format | Article |
| series | SN Applied Sciences |
| spelling | doaj.art-d2758870edd14c238a4e1a6820da4a642023-01-15T12:18:25ZengSpringerSN Applied Sciences2523-39632523-39712023-01-015211110.1007/s42452-023-05277-zAffective computing model for natural interaction based on large-scale self-built datasetJin Lu0Xiaoting Wan1Guangdong Key Laboratory of Big Data Intelligence for Vocational Education, Shenzhen PolytechnicGuangdong Key Laboratory of Big Data Intelligence for Vocational Education, Shenzhen PolytechnicArticle Highlights 1. In this paper, we describe a way to make a new face expression dataset by putting together open source and self-collected datasets. 2. This allows us to create the largest face expression dataset in the industry, which is better in quality and has more data. 3. When we use this new dataset to train a face expression recognition model, it performs the best in the current industry.https://doi.org/10.1007/s42452-023-05277-zExpression recognitionExpression datasetDeep network structureNeural network |
| spellingShingle | Jin Lu Xiaoting Wan Affective computing model for natural interaction based on large-scale self-built dataset SN Applied Sciences Expression recognition Expression dataset Deep network structure Neural network |
| title | Affective computing model for natural interaction based on large-scale self-built dataset |
| title_full | Affective computing model for natural interaction based on large-scale self-built dataset |
| title_fullStr | Affective computing model for natural interaction based on large-scale self-built dataset |
| title_full_unstemmed | Affective computing model for natural interaction based on large-scale self-built dataset |
| title_short | Affective computing model for natural interaction based on large-scale self-built dataset |
| title_sort | affective computing model for natural interaction based on large scale self built dataset |
| topic | Expression recognition Expression dataset Deep network structure Neural network |
| url | https://doi.org/10.1007/s42452-023-05277-z |
| work_keys_str_mv | AT jinlu affectivecomputingmodelfornaturalinteractionbasedonlargescaleselfbuiltdataset AT xiaotingwan affectivecomputingmodelfornaturalinteractionbasedonlargescaleselfbuiltdataset |