A fusion of CNN And SIFT For multicultural facial expression recognition
The most generic and understandable way of communication is by observing facial expressions; Facial Expression Recognition(FER) performance was affected by the differences in ethnicity, culture, and geography. This research proposes a feature grafting-based novel technique to recognize multicultural...
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| Published in | Multimedia tools and applications Vol. 84; no. 28; pp. 33505 - 33523 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.08.2025
Springer Nature B.V |
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| Abstract | The most generic and understandable way of communication is by observing facial expressions; Facial Expression Recognition(FER) performance was affected by the differences in ethnicity, culture, and geography. This research proposes a feature grafting-based novel technique to recognize multicultural facial expressions. The Viola-Jones algorithm detects the facial region of an image that is most concentrated. Feature Grafting is used to combine high-level and low-level features. A multicultural facial expression database was created using still images of male and female subjects obtained from three datasets: TFEID, Radboud, and JAFFE. The model is designed and tested using this dataset and the proposed technique. The obtained accuracy using the proposed technique was 94.34% which is better than the state-of-the-art method. |
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| AbstractList | The most generic and understandable way of communication is by observing facial expressions; Facial Expression Recognition(FER) performance was affected by the differences in ethnicity, culture, and geography. This research proposes a feature grafting-based novel technique to recognize multicultural facial expressions. The Viola-Jones algorithm detects the facial region of an image that is most concentrated. Feature Grafting is used to combine high-level and low-level features. A multicultural facial expression database was created using still images of male and female subjects obtained from three datasets: TFEID, Radboud, and JAFFE. The model is designed and tested using this dataset and the proposed technique. The obtained accuracy using the proposed technique was 94.34% which is better than the state-of-the-art method. |
| Author | Qadir, Inam Iqbal, Muhammad Amjad Ashraf, Samman Akram, Sheeraz |
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| SubjectTerms | Accuracy Algorithms Artificial intelligence Classification Computer Communication Networks Computer Science Data Structures and Information Theory Datasets Emotions Face recognition Grafting Machine learning Multiculturalism & pluralism Multimedia Information Systems Neural networks Pattern recognition Special Purpose and Application-Based Systems Support vector machines White people |
| Title | A fusion of CNN And SIFT For multicultural facial expression recognition |
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