RPG-Palm: Realistic Pseudo-data Generation for Palmprint Recognition
Lei Shen, Jianlong Jin, Ruixin Zhang, Huaen Li, Kai Zhao, Yingyi, Zhang, Jingyun Zhang, Shouhong Ding, Yang Zhao, Wei Jia

TL;DR
This paper introduces RPG-Palm, a novel pseudo-data generation model for palmprint recognition that synthesizes realistic palmprints to enhance recognition performance and reduce dependence on large real datasets.
Contribution
The paper presents a new generative model with a conditional modulation generator and identity-aware loss to produce diverse, identity-consistent palmprints, advancing palmprint recognition research.
Findings
Synthetic palmprints significantly improve recognition accuracy.
The model outperforms state-of-the-art methods by over 5% and 14%.
Effective even with only 10% real training data.
Abstract
Palmprint recently shows great potential in recognition applications as it is a privacy-friendly and stable biometric. However, the lack of large-scale public palmprint datasets limits further research and development of palmprint recognition. In this paper, we propose a novel realistic pseudo-palmprint generation (RPG) model to synthesize palmprints with massive identities. We first introduce a conditional modulation generator to improve the intra-class diversity. Then an identity-aware loss is proposed to ensure identity consistency against unpaired training. We further improve the B\'ezier palm creases generation strategy to guarantee identity independence. Extensive experimental results demonstrate that synthetic pretraining significantly boosts the recognition model performance. For example, our model improves the state-of-the-art B\'ezierPalm by more than and in terms…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsBiometric Identification and Security · Forensic and Genetic Research
MethodsPathways Language Model · Additive Angular Margin Loss
