Canny2Palm: Realistic and Controllable Palmprint Generation for Large-scale Pre-training

TL;DR

Canny2Palm is a novel method for generating realistic and diverse palmprint images, significantly enhancing pre-training for palmprint recognition and outperforming existing methods on benchmark datasets.

Contribution

The paper introduces Canny2Palm, a new synthesis approach that creates controllable, realistic palmprint data for large-scale pre-training, improving recognition accuracy.

Findings

Pre-trained models with Canny2Palm data outperform state-of-the-art by up to 7.2%

Canny2Palm enables generation of large-scale, diverse identities for pre-training

Performance continues to improve with more synthetic identities, showing scalability

Abstract

Palmprint recognition is a secure and privacy-friendly method of biometric identification. One of the major challenges to improve palmprint recognition accuracy is the scarcity of palmprint data. Recently, a popular line of research revolves around the synthesis of virtual palmprints for large-scale pre-training purposes. In this paper, we propose a novel synthesis method named Canny2Palm that extracts palm textures with Canny edge detector and uses them to condition a Pix2Pix network for realistic palmprint generation. By re-assembling palmprint textures from different identities, we are able to create new identities by seeding the generator with new assemblies. Canny2Palm not only synthesizes realistic data following the distribution of real palmprints but also enables controllable diversity to generate large-scale new identities. On open-set palmprint recognition benchmarks, models pre-trained with Canny2Palm synthetic data outperform the state-of-the-art with up to 7.2% higher identification accuracy. Moreover, the performance of models pre-trained with Canny2Palm continues to improve given 10,000 synthetic IDs while those with existing methods already saturate, demonstrating the potential of our method for large-scale pre-training.