Multiple Training Stage Image Enhancement Enrolled With CCRGAN Pseudo Templates for Large Area Dry Fingerprint Recognition

Abstract

Fingerprint recognition is widely used in daily life. However, the physiological phenomena of human can still fail recognition under dry and low temperature conditions. We present two main approaches for enhancing the accuracy of fingerprint recognition systems in cases of dry fingerprints. The first approach is an image enhancement algorithm with multiple training stages (MTS) for fingerprints at low temperatures, which aims to restore low quality fingerprints to normal quality. The second method is a Cycled Contrastive Ridge Generative Adversarial Network (CCRGAN) learning framework for synthesizing enrolled templates, aimed at improving the accuracy of low-temperature fingerprint matching in authentication systems. For FVC2002 dataset, we have achieved 0.05% for EER on DB1A, which is better than 0.09% for Li et al. (2022) and 0.18% for Wong and Lai (2020) on the MTS algorithm. After adding CCRGAN pseudo low fingerprint, the EER is improved to 0.0009%. As for inference time, our inference time is about 83 times faster than Li et al. (2022) on the FVC2002 DB3B.

Keywords

• Convolution
• generators
• image processing
• image enhancement
• neural networks