PTH-Net : Dynamic Facial Expression Recognition without Face Detection and Alignment
Pyramid Temporal Hierarchy Network (PTH-Net) is a new paradigm for dynamic facial expression recognition, applied directly to raw videos, without face detection and alignment. Unlike the traditional paradigm, which focus only on facial areas and often overlooks valuable information like body movemen...
| Publié dans: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - PP(2024) vom: 28. Nov. |
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| Auteur principal: | |
| Autres auteurs: | , , , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2024
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| Accès à la collection: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Sujets: | Journal Article |
| Résumé: | Pyramid Temporal Hierarchy Network (PTH-Net) is a new paradigm for dynamic facial expression recognition, applied directly to raw videos, without face detection and alignment. Unlike the traditional paradigm, which focus only on facial areas and often overlooks valuable information like body movements, PTH-Net preserves more critical information. It does this by distinguishing between backgrounds and human bodies at the feature level, offering greater flexibility as an end-to-end network. Specifically, PTH-Net utilizes a pre-trained backbone to extract multiple general features of video understanding at various temporal frequencies, forming a temporal feature pyramid. It then further expands this temporal hierarchy through differentiated parameter sharing and downsampling, ultimately refining emotional information under the supervision of expression temporal-frequency invariance. Additionally, PTH-Net features an efficient Scalable Semantic Distinction layer that enhances feature discrimination, helping to better identify target expressions versus non-target ones in the video. Finally, extensive experiments demonstrate that PTH-Net performs excellently in eight challenging benchmarks, with lower computational costs compared to previous methods. The source code is available at https://github.com/lm495455/PTH-Net |
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| Description: | Date Revised 03.03.2025 published: Print-Electronic Citation Status Publisher |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2024.3504298 |