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Efficient, Validation-Free Intrinsic Quality Estimation for Large-Scale Face Recognition Datasets

2026-05-28 10:15:46
Zhichao Chen, Yongle Zhao, Kaicheng Yang, Meng Yang, Yin Xie, Ziyong Feng
arXiv_CV
           
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2605.29720

PDF

https://arxiv.org/pdf/2605.29720.pdf

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