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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.00250 |
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| _version_ | 1866911768844959744 |
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| author | Wang, Zhifeng Zhang, Kaihao Sankaranarayana, Ramesh |
| author_facet | Wang, Zhifeng Zhang, Kaihao Sankaranarayana, Ramesh |
| contents | This study introduces LRDif, a novel diffusion-based framework designed specifically for facial expression recognition (FER) within the context of under-display cameras (UDC). To address the inherent challenges posed by UDC's image degradation, such as reduced sharpness and increased noise, LRDif employs a two-stage training strategy that integrates a condensed preliminary extraction network (FPEN) and an agile transformer network (UDCformer) to effectively identify emotion labels from UDC images. By harnessing the robust distribution mapping capabilities of Diffusion Models (DMs) and the spatial dependency modeling strength of transformers, LRDif effectively overcomes the obstacles of noise and distortion inherent in UDC environments. Comprehensive experiments on standard FER datasets including RAF-DB, KDEF, and FERPlus, LRDif demonstrate state-of-the-art performance, underscoring its potential in advancing FER applications. This work not only addresses a significant gap in the literature by tackling the UDC challenge in FER but also sets a new benchmark for future research in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00250 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LRDif: Diffusion Models for Under-Display Camera Emotion Recognition Wang, Zhifeng Zhang, Kaihao Sankaranarayana, Ramesh Computer Vision and Pattern Recognition This study introduces LRDif, a novel diffusion-based framework designed specifically for facial expression recognition (FER) within the context of under-display cameras (UDC). To address the inherent challenges posed by UDC's image degradation, such as reduced sharpness and increased noise, LRDif employs a two-stage training strategy that integrates a condensed preliminary extraction network (FPEN) and an agile transformer network (UDCformer) to effectively identify emotion labels from UDC images. By harnessing the robust distribution mapping capabilities of Diffusion Models (DMs) and the spatial dependency modeling strength of transformers, LRDif effectively overcomes the obstacles of noise and distortion inherent in UDC environments. Comprehensive experiments on standard FER datasets including RAF-DB, KDEF, and FERPlus, LRDif demonstrate state-of-the-art performance, underscoring its potential in advancing FER applications. This work not only addresses a significant gap in the literature by tackling the UDC challenge in FER but also sets a new benchmark for future research in the field. |
| title | LRDif: Diffusion Models for Under-Display Camera Emotion Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.00250 |