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; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 19605-19616

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 intra-class diversity. Then an identity-aware loss is proposed to ensure identity consistency against unpaired training. We further improve the Bezier 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 BezierPalm 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. The code will be made open upon acceptance.