CPGAN: Collective Punishment Generative Adversarial Network for Dry Fingerprint Image Enhancement

• Published in: 2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS) pp. 1 - 5
• Main Authors: Su, Yu-Chi, Chiu, Ching-Te, Cheng, Chih-Han, Liu, Kuan-Hsien, Lee, Tsung-Chan, Chen, Jia-Lin, Luo, Jie-Yu, Chung, Wei-Chang, Chang, Yao-Ren, Ho, Kuan-Ying
• Format: Conference Proceeding
• Language: English
• Published: IEEE 11.06.2023
• Subjects: Circuits and systems; Convolution Neural Network; Dry fingerprints; Feature extraction; Fingerprint recognition; GAN; Generative adversarial networks; Image enhancement; Image matching; Image recognition; Neural networks
• ISSN: 2834-9857
• DOI: 10.1109/AICAS57966.2023.10168628

Fingerprint has been widely used in our daily life, such as mobile. However, some circumstances may lead to low unlocking rate, like fingerprint at low temperature(dry fingerprint) or washed fingerprint. Our method mainly focuses on the former by making it close to normal temperature fingerprint. The main idea of our method, which called "CPGAN", is to improve GAN to boost the quality of the enhanced fingerprint. Our objective is to make the generator generates the high quality of enhanced fingerprint. The method is divided into two parts: "strengthening the discriminator" and "strengthening the generator". For strengthening the generator, we adopt the mechanism of "Collective Punishment" to our work. For strengthening the discriminator, we utilize two generators and feature extractor to boost the discriminator. In our experiments, the results surpass the state-of-the-arts on FVC2002 about 75%.