A Dense Pyramid Convolution Network for Infant Fingerprint Super-Resolution and Enhancement

Abstract

Fingerprint recognition has been widely investigated and achieved great success for personal recognition. Most of existing fingerprint recognition algorithms can work well on adults but cannot be directly used for children, especially for infants. Compared with adult fingerprints, the size of infant fingerprints is smaller with lower resolution under the same acquisition conditions. In addition, infant fingerprint images suffer from various degradations from the physiological effects and bad collection conditions. Some studies focused on using high-quality and high-resolution sensors to capture infant fingerprints for reliable recognition, which will increase the costs. In this paper, we propose a deep learning based method to perform the super-resolution and enhancement of infant fingerprints by an end-to-end way for more reliable recognition, which is compatible with the existing recognition system. In this method, a dense pyramid convolution neural network is built for joint deep learning of fingerprint super-resolution and enhancement, with a minutia attention block added for more accurate reconstruction of local details. The network is trained with adult fingerprints for image transformation and tested on infant fingerprint dataset. Experimental results show that the proposed method achieves promising improvements for infant fingerprint recognition.