Smile detection using hybrid face representation
Smile detection has attracted considerable amount of research interests in the domain of computer vision. It possesses several potential applications in gaming, human-to-computer and human-to-human interaction. This paper investigates the challenging problem of smile detection from face images acqui...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2016
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/53835/1/Smile%20detection%20using%20hybrid%20face%20representation.pdf |
| _version_ | 1796975964860710912 |
|---|---|
| author | Arigbabu, Olasimbo Ayodeji Mahmood, Saif Syed Ahmad Abdul Rahman, Sharifah Mumtazah Arigbabu, Abayomi A. |
| author_facet | Arigbabu, Olasimbo Ayodeji Mahmood, Saif Syed Ahmad Abdul Rahman, Sharifah Mumtazah Arigbabu, Abayomi A. |
| author_sort | Arigbabu, Olasimbo Ayodeji |
| collection | UPM |
| description | Smile detection has attracted considerable amount of research interests in the domain of computer vision. It possesses several potential applications in gaming, human-to-computer and human-to-human interaction. This paper investigates the challenging problem of smile detection from face images acquired under unconstrained conditions. First, a locally weighted multiblock shape-texture descriptor is proposed to extract detailed local and global information from faces with diverse variations such as orientation, illumination, pose, and occlusion. The proposed technique combines the robustness of pyramid histogram of oriented gradient and local binary pattern for image feature representation using an adaptive local weight assignment. By locally weighting the descriptors from very dense patches of the image, we induce discriminating local spatial context to the distribution of the descriptions from the face image. Second, in order to minimize redundancy and extract the most relevant facial information from the feature vectors, a correlation based filter feature selection approach is adopted. Finally, kernel based classifiers such as support vector machine and kernel extreme learning machine are utilized for performing classification. Based on our findings, the proposed framework provides very competitive detection rate to related approaches that have exploited image alignment as an important stage for improving performance of smile detection. |
| first_indexed | 2024-03-06T09:18:58Z |
| format | Article |
| id | upm.eprints-53835 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T09:18:58Z |
| publishDate | 2016 |
| publisher | Springer |
| record_format | dspace |
| spelling | upm.eprints-538352018-02-12T04:34:21Z http://psasir.upm.edu.my/id/eprint/53835/ Smile detection using hybrid face representation Arigbabu, Olasimbo Ayodeji Mahmood, Saif Syed Ahmad Abdul Rahman, Sharifah Mumtazah Arigbabu, Abayomi A. Smile detection has attracted considerable amount of research interests in the domain of computer vision. It possesses several potential applications in gaming, human-to-computer and human-to-human interaction. This paper investigates the challenging problem of smile detection from face images acquired under unconstrained conditions. First, a locally weighted multiblock shape-texture descriptor is proposed to extract detailed local and global information from faces with diverse variations such as orientation, illumination, pose, and occlusion. The proposed technique combines the robustness of pyramid histogram of oriented gradient and local binary pattern for image feature representation using an adaptive local weight assignment. By locally weighting the descriptors from very dense patches of the image, we induce discriminating local spatial context to the distribution of the descriptions from the face image. Second, in order to minimize redundancy and extract the most relevant facial information from the feature vectors, a correlation based filter feature selection approach is adopted. Finally, kernel based classifiers such as support vector machine and kernel extreme learning machine are utilized for performing classification. Based on our findings, the proposed framework provides very competitive detection rate to related approaches that have exploited image alignment as an important stage for improving performance of smile detection. Springer 2016-06 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/53835/1/Smile%20detection%20using%20hybrid%20face%20representation.pdf Arigbabu, Olasimbo Ayodeji and Mahmood, Saif and Syed Ahmad Abdul Rahman, Sharifah Mumtazah and Arigbabu, Abayomi A. (2016) Smile detection using hybrid face representation. Journal of Ambient Intelligence and Humanized Computing, 7 (3). pp. 415-426. ISSN 1868-5137; ESSN: 1868-5145 https://link.springer.com/article/10.1007/s12652-015-0333-4 10.1007/s12652-015-0333-4 |
| spellingShingle | Arigbabu, Olasimbo Ayodeji Mahmood, Saif Syed Ahmad Abdul Rahman, Sharifah Mumtazah Arigbabu, Abayomi A. Smile detection using hybrid face representation |
| title | Smile detection using hybrid face representation |
| title_full | Smile detection using hybrid face representation |
| title_fullStr | Smile detection using hybrid face representation |
| title_full_unstemmed | Smile detection using hybrid face representation |
| title_short | Smile detection using hybrid face representation |
| title_sort | smile detection using hybrid face representation |
| url | http://psasir.upm.edu.my/id/eprint/53835/1/Smile%20detection%20using%20hybrid%20face%20representation.pdf |
| work_keys_str_mv | AT arigbabuolasimboayodeji smiledetectionusinghybridfacerepresentation AT mahmoodsaif smiledetectionusinghybridfacerepresentation AT syedahmadabdulrahmansharifahmumtazah smiledetectionusinghybridfacerepresentation AT arigbabuabayomia smiledetectionusinghybridfacerepresentation |