arXiv (All)
open access
Efficient, Validation-Free Intrinsic Quality Estimation for Large-Scale Face Recognition Datasets
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
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Learning Steadily: Accumulating Relative Point Margin Scores for Face Image Quality Assessment#1115618
- Evaluating Proposed Fairness Models for Face Recognition Algorithms#925689
- DenseFace: Bias Mitigation in Face Recognition via Density-Aware Probabilistic Matching#1110529
- What makes you, you? Analyzing Recognition by Swapping Face Parts#250586
- AI-Facial Composite Accuracy#27639
Record · ID 800044
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive