atlas

Research map / Label-efficient and robust learning / Recognition and long-tail

SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness

CVPR 2020 · Label-efficient and robust learning › Recognition and long-tail · approach: Fair and quality-aware face recognition · 224 citations (OpenAlex)

Check your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the deployed face recognition model for quality assessment instead of a separately trained on-top modelSER-FIQ · Adience and LFW, cross-database evaluationface verification error after rejecting low-quality imagesHelped
Estimate quality with SER-FIQ on-top model rather than the evaluated baselinesFaceNet and ArcFace embeddings · Adience and LFW, cross-database evaluationface verification error after rejecting low-quality imagesHelped
Use no-reference image quality metrics (Brisque, Niqe, or Piqe) for face quality assessmentFace verification · Adience and LFWverification error after rejecting low-quality imagesHurt
Use COTS quality scores with ArcFace embeddingsFace verification · Adience and LFWface verification performance after rejecting low-quality imagesNo clear effect
Train a supervised quality predictor using ColorFeret-derived labelsBest-Rowden and Jain approach · Adience, FaceNet embeddingsface verification error after rejecting low-quality imagesMixed
Train FaceQnet on quality labels derived from FaceNetFaceQnet · LFW and Adience, FaceNet embeddingsface verification error after rejecting low-quality imagesMixed

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy