Latent Diffusion Based Face Enhancement under Degraded Conditions for Forensic Face Recognition
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911087919628288 |
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| author | Ugail, Hassan Alawar, Hamad Mansour Zehi, AbdulNasser Abbas Alkendi, Ahmed Mohammad Jaleel, Ismail Lujain |
| author_facet | Ugail, Hassan Alawar, Hamad Mansour Zehi, AbdulNasser Abbas Alkendi, Ahmed Mohammad Jaleel, Ismail Lujain |
| contents | Face recognition systems experience severe performance degradation when processing low-quality forensic evidence imagery. This paper presents an evaluation of latent diffusion-based enhancement for improving face recognition under forensically relevant degradations. Using a dataset of 3,000 individuals from LFW with 24,000 recognition attempts, we implement the Flux.1 Kontext Dev pipeline with Facezoom LoRA adaptation to test against seven degradation categories, including compression artefacts, blur effects, and noise contamination. Our approach demonstrates substantial improvements, increasing overall recognition accuracy from 29.1% to 84.5% (55.4 percentage point improvement, 95% CI: [54.1, 56.7]). Statistical analysis reveals significant performance gains across all degradation types, with effect sizes exceeding conventional thresholds for practical significance. These findings establish the potential of sophisticated diffusion based enhancement in forensic face recognition applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00941 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Latent Diffusion Based Face Enhancement under Degraded Conditions for Forensic Face Recognition Ugail, Hassan Alawar, Hamad Mansour Zehi, AbdulNasser Abbas Alkendi, Ahmed Mohammad Jaleel, Ismail Lujain Computer Vision and Pattern Recognition Face recognition systems experience severe performance degradation when processing low-quality forensic evidence imagery. This paper presents an evaluation of latent diffusion-based enhancement for improving face recognition under forensically relevant degradations. Using a dataset of 3,000 individuals from LFW with 24,000 recognition attempts, we implement the Flux.1 Kontext Dev pipeline with Facezoom LoRA adaptation to test against seven degradation categories, including compression artefacts, blur effects, and noise contamination. Our approach demonstrates substantial improvements, increasing overall recognition accuracy from 29.1% to 84.5% (55.4 percentage point improvement, 95% CI: [54.1, 56.7]). Statistical analysis reveals significant performance gains across all degradation types, with effect sizes exceeding conventional thresholds for practical significance. These findings establish the potential of sophisticated diffusion based enhancement in forensic face recognition applications. |
| title | Latent Diffusion Based Face Enhancement under Degraded Conditions for Forensic Face Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.00941 |