A multi-task minutiae transformer network for fingerprint recognition of young children

Fingerprint recognition of children have attracted increasing attention for real applications such as identity certificate. However, the recognition performance is greatly reduced if the existing systems are directly used on the fingerprints of young children due to their low resolution and poor image quality. Towards more accurate fingerprint recognition of young children, this paper proposes multi-task deep learning framework based on Pyramid Densely-connected U-shaped Swin-transformer network (PDUSwin-Net) to jointly learn the reconstruction of enhanced high-resolution images and detection of minutiae points, which is compatible with existing adult fingerprint sensors (500 dpi) and minutiae matchers. First, a pyramid densely-connected Ushaped convolutional network is proposed to learn the features of fingerprints for multiple tasks. Then, a swin-transformer attention block is added to model the correlations of long-spatial features. In the decoding part, two branches are built for the tasks of fingerprint enhancement and minutiae extraction. Finally, our method is tested with the existing matchers on two independent fingerprint datasets of young children aged from 0-2 years. Results and comparison show that our method performs better than other methods for fingerprint recognition of young children.