RPG-Palm: Realistic Pseudo-data Generation for Palmprint Recognition

• URL: http://arxiv.org/abs/2307.14016v3
• Date: Tue, 8 Aug 2023 07:57:15 GMT
• Title: RPG-Palm: Realistic Pseudo-data Generation for Palmprint Recognition
• Authors: Lei Shen, Jianlong Jin, Ruixin Zhang, Huaen Li, Kai Zhao, Yingyi Zhang, Jingyun Zhang, Shouhong Ding, Yang Zhao, Wei Jia
• Abstract summary: We propose a novel realistic pseudo-palmprint generation model to synthesize palmprints with massive identities. Our model improves the state-of-the-art B'ezierPalm by more than $5%$ and $14%$ in terms of TAR@FAR=1e-6 under the $1:1$ and $1:3$ Open-set protocol. Our method still outperforms ArcFace with $100%$ real training data, indicating that we are closer to real-data-free palmprint recognition.
• Score: 26.82617300073746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 $5\%$ and $14\%$ in terms of TAR@FAR=1e-6 under the $1:1$ and $1:3$ Open-set protocol. When accessing only $10\%$ of the real training data, our method still outperforms ArcFace with $100\%$ real training data, indicating that we are closer to real-data-free palmprint recognition.

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