Facial Image Feature Analysis and its Specialization for Fréchet Distance and Neighborhoods
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917706022780928 |
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| author | Cetin, Doruk Schesch, Benedikt Stamenkovic, Petar Huber, Niko Benjamin Zünd, Fabio Helou, Majed El |
| author_facet | Cetin, Doruk Schesch, Benedikt Stamenkovic, Petar Huber, Niko Benjamin Zünd, Fabio Helou, Majed El |
| contents | Assessing distances between images and image datasets is a fundamental task in vision-based research. It is a challenging open problem in the literature and despite the criticism it receives, the most ubiquitous method remains the Fréchet Inception Distance. The Inception network is trained on a specific labeled dataset, ImageNet, which has caused the core of its criticism in the most recent research. Improvements were shown by moving to self-supervision learning over ImageNet, leaving the training data domain as an open question. We make that last leap and provide the first analysis on domain-specific feature training and its effects on feature distance, on the widely-researched facial image domain. We provide our findings and insights on this domain specialization for Fréchet distance and image neighborhoods, supported by extensive experiments and in-depth user studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18430 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Facial Image Feature Analysis and its Specialization for Fréchet Distance and Neighborhoods Cetin, Doruk Schesch, Benedikt Stamenkovic, Petar Huber, Niko Benjamin Zünd, Fabio Helou, Majed El Computer Vision and Pattern Recognition Assessing distances between images and image datasets is a fundamental task in vision-based research. It is a challenging open problem in the literature and despite the criticism it receives, the most ubiquitous method remains the Fréchet Inception Distance. The Inception network is trained on a specific labeled dataset, ImageNet, which has caused the core of its criticism in the most recent research. Improvements were shown by moving to self-supervision learning over ImageNet, leaving the training data domain as an open question. We make that last leap and provide the first analysis on domain-specific feature training and its effects on feature distance, on the widely-researched facial image domain. We provide our findings and insights on this domain specialization for Fréchet distance and image neighborhoods, supported by extensive experiments and in-depth user studies. |
| title | Facial Image Feature Analysis and its Specialization for Fréchet Distance and Neighborhoods |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.18430 |