WaFusion: A Wavelet-Enhanced Diffusion Framework for Face Morph Generation
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918095512141824 |
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| author | Hosseini, Seyed Rasoul Ahmadieh, Omid Dawson, Jeremy Nasrabadi, Nasser |
| author_facet | Hosseini, Seyed Rasoul Ahmadieh, Omid Dawson, Jeremy Nasrabadi, Nasser |
| contents | Biometric face morphing poses a critical challenge to identity verification systems, undermining their security and robustness. To address this issue, we propose WaFusion, a novel framework combining wavelet decomposition and diffusion models to generate high-quality, realistic morphed face images efficiently. WaFusion leverages the structural details captured by wavelet transforms and the generative capabilities of diffusion models, producing face morphs with minimal artifacts. Experiments conducted on FERET, FRGC, FRLL, and WVU Twin datasets demonstrate WaFusion's superiority over state-of-the-art methods, producing high-resolution morphs with fewer artifacts. Our framework excels across key biometric metrics, including the Attack Presentation Classification Error Rate (APCER), Bona Fide Presentation Classification Error Rate (BPCER), and Equal Error Rate (EER). This work sets a new benchmark in biometric morph generation, offering a cutting-edge and efficient solution to enhance biometric security systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12493 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WaFusion: A Wavelet-Enhanced Diffusion Framework for Face Morph Generation Hosseini, Seyed Rasoul Ahmadieh, Omid Dawson, Jeremy Nasrabadi, Nasser Graphics Biometric face morphing poses a critical challenge to identity verification systems, undermining their security and robustness. To address this issue, we propose WaFusion, a novel framework combining wavelet decomposition and diffusion models to generate high-quality, realistic morphed face images efficiently. WaFusion leverages the structural details captured by wavelet transforms and the generative capabilities of diffusion models, producing face morphs with minimal artifacts. Experiments conducted on FERET, FRGC, FRLL, and WVU Twin datasets demonstrate WaFusion's superiority over state-of-the-art methods, producing high-resolution morphs with fewer artifacts. Our framework excels across key biometric metrics, including the Attack Presentation Classification Error Rate (APCER), Bona Fide Presentation Classification Error Rate (BPCER), and Equal Error Rate (EER). This work sets a new benchmark in biometric morph generation, offering a cutting-edge and efficient solution to enhance biometric security systems. |
| title | WaFusion: A Wavelet-Enhanced Diffusion Framework for Face Morph Generation |
| topic | Graphics |
| url | https://arxiv.org/abs/2507.12493 |