Coupling deep and handcrafted features to assess smile genuineness
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913747813007360 |
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| author | Pawlus, Benedykt Smolka, Bogdan Kawulok, Jolanta Kawulok, Michal |
| author_facet | Pawlus, Benedykt Smolka, Bogdan Kawulok, Jolanta Kawulok, Michal |
| contents | Assessing smile genuineness from video sequences is a vital topic concerned with recognizing facial expression and linking them with the underlying emotional states. There have been a number of techniques proposed underpinned with handcrafted features, as well as those that rely on deep learning to elaborate the useful features. As both of these approaches have certain benefits and limitations, in this work we propose to combine the features learned by a long short-term memory network with the features handcrafted to capture the dynamics of facial action units. The results of our experiments indicate that the proposed solution is more effective than the baseline techniques and it allows for assessing the smile genuineness from video sequences in real-time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16128 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Coupling deep and handcrafted features to assess smile genuineness Pawlus, Benedykt Smolka, Bogdan Kawulok, Jolanta Kawulok, Michal Computer Vision and Pattern Recognition Assessing smile genuineness from video sequences is a vital topic concerned with recognizing facial expression and linking them with the underlying emotional states. There have been a number of techniques proposed underpinned with handcrafted features, as well as those that rely on deep learning to elaborate the useful features. As both of these approaches have certain benefits and limitations, in this work we propose to combine the features learned by a long short-term memory network with the features handcrafted to capture the dynamics of facial action units. The results of our experiments indicate that the proposed solution is more effective than the baseline techniques and it allows for assessing the smile genuineness from video sequences in real-time. |
| title | Coupling deep and handcrafted features to assess smile genuineness |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.16128 |