Efficient, Validation-Free Intrinsic Quality Estimation for Large-Scale Face Recognition Datasets
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917543415906304 |
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| author | Chen, Zhichao Zhao, Yongle Yang, Kaicheng Yang, Meng Xie, Yin Feng, Ziyong |
| author_facet | Chen, Zhichao Zhao, Yongle Yang, Kaicheng Yang, Meng Xie, Yin Feng, Ziyong |
| contents | We propose Intrinsic Quality (IQ), a validation-free metric designed to estimate the inherent potential of face recognition (FR) datasets to produce high-performance models without the need for full-scale training. IQ integrates two components: (i) a Neighbor-Consistency Score that quantifies local identity label agreement via nearest neighbors, and (ii) Global Representation Subspace Complexity (Effective Rank, ER), which captures the underlying embedding geometry and dataset diversity. IQ allows for rapid evaluation using lightweight proxy models or data subsets, facilitating dataset diagnosis and curation prior to resource-intensive full-scale training. We describe an experimental protocol tailored to clean, noisy, and mixed-quality FR datasets, and outline evaluation methodologies to validate IQ's predictive power for downstream performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_29720 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Efficient, Validation-Free Intrinsic Quality Estimation for Large-Scale Face Recognition Datasets Chen, Zhichao Zhao, Yongle Yang, Kaicheng Yang, Meng Xie, Yin Feng, Ziyong Computer Vision and Pattern Recognition Machine Learning We propose Intrinsic Quality (IQ), a validation-free metric designed to estimate the inherent potential of face recognition (FR) datasets to produce high-performance models without the need for full-scale training. IQ integrates two components: (i) a Neighbor-Consistency Score that quantifies local identity label agreement via nearest neighbors, and (ii) Global Representation Subspace Complexity (Effective Rank, ER), which captures the underlying embedding geometry and dataset diversity. IQ allows for rapid evaluation using lightweight proxy models or data subsets, facilitating dataset diagnosis and curation prior to resource-intensive full-scale training. We describe an experimental protocol tailored to clean, noisy, and mixed-quality FR datasets, and outline evaluation methodologies to validate IQ's predictive power for downstream performance. |
| title | Efficient, Validation-Free Intrinsic Quality Estimation for Large-Scale Face Recognition Datasets |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2605.29720 |