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arXiv:2611.11391 · v1

Let the Gallery Learn Itself: Training-Free Iterative Side-View Face Recognition

Zijun HuangSuncheng Xiang
cs.CV
首次提交 2026-11-08 21:59
版本更新 2026-11-08 21:59
雷达观测 2026-11-10 12:16
arXiv ↗PDF ↗

摘要

What if a face recognition system could learn from every encounter without a single gradient update? We present Gallery Self-Evolution (GSE), a training-free framework where the gallery itself adapts at test time. The core mechanism---dedup-FIFO curation---archives novel appearance samples after successful recognition while permanently locking the enrollment template, bounded at 30 embeddings per identity. On CFP-FP and IJB-C benchmarks, GSE closes the side-view gap by +23 points at 45° yaw and boosts TAR@FAR=0.001 by +9.4 points, all at CPU-level cost. No retraining. No GPU. Just a gallery that grows smarter every time it meets you.

作者备注：Work in progress

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v1 · 2026-11-08 21:59 · 最新观测版本

Let the Gallery Learn Itself: Training-Free Iterative Side-View Face Recognition

What if a face recognition system could learn from every encounter without a single gradient update? We present Gallery Self-Evolution (GSE), a training-free framework where the gallery itself adapts at test time. The core mechanism---dedup-FIFO curation---archives novel appearance samples after successful recognition while permanently locking the enrollment template, bounded at 30 embeddings per identity. On CFP-FP and IJB-C benchmarks, GSE closes the side-view gap by +23 points at 45° yaw and boosts TAR@FAR=0.001 by +9.4 points, all at CPU-level cost. No retraining. No GPU. Just a gallery that grows smarter every time it meets you.

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