Bézier Palm: A Free Lunch for Palmprint Recognition

1. Tencent Youtu Lab, 2. UCLA, 3. Hefei University of Technology, 4. Shanghai Jiaotong University.

# Abstract:

Palmprints are private and stable information for biometric recognition. In the deep learning era, the development of palmprint recognition is limited by the lack of sufficient training data. In this paper, by observing that palmar creases are the key information to deep-learning-based palmprint recognition, we propose to synthesize training data by manipulating palmar creases. Concretely, we introduce an intuitive geometric model which represents palmar creases with parameterized Bézier curves. By randomly sampling Bézier parameters, we can synthesize massive training samples of diverse identities, which enables us to pretrain large-scale palmprint recognition models. Experimental results demonstrate that such synthetically pretrained models have a very strong generalization ability: they can be efficiently transferred to real datasets, leading to significant performance improvements on palmprint recognition. For example, under the open-set protocol, our method improves the strong ArcFace baseline by more than 10% in terms of [email protected] And under the closed-set protocol, our method reduces the equal error rate (EER) by an order of magnitude.

# Synthesized Samples:

## Synthesize in 2D:

The figure below provides some synthesized samples, each row contains sample of the same identity.

TBD

# Experimental Results

## [email protected] curve on public datasets.

• AF: ArcFace
• MF: MobileFaceNet backbone;
• R50: ResNet-50 backbone;

Our method consistently outperforms the baseline with substantial margin.

## ImageNet pretrained v.s. our synthetically pretrained.

Compared to ImageNet pretraining, our synthetically pretrained models generalize better when finetuning on real palmprint recognition datasets.

# Citation:

@article{zhao2022geometric,
}
@article{shen2022distribution,
}