Geometric Synthesis: A Free lunch for Large-scale Palmprint Recognition Model Pretraining

TL;DR

This paper introduces a geometric data synthesis method for palmprint recognition, enabling large-scale pretraining that significantly improves recognition accuracy and generalization to real datasets.

Contribution

It proposes a novel geometric model using Bézier curves to synthesize diverse palmprint training data, enhancing model pretraining and transferability.

Findings

Pretrained models show strong generalization to real datasets.

Significant accuracy improvements over baseline methods.

Reduces error rates substantially in palmprint recognition.

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 B\'ezier curves. By randomly sampling B\'ezier 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 TAR@1e-6. And under the closed-set protocol, our method reduces the equal error rate (EER) by an order of magnitude.