Finger-UNet: A U-Net based Multi-Task Architecture for Deep Fingerprint
  Enhancement
Ekta Gavas Anoop Namboodiri
For decades, fingerprint recognition has been prevalent for security,
forensics, and other biometric applications. However, the availability of
good-quality fingerprints is challenging, making recognition difficult.
Fingerprint images might be degraded with a poor ridge structure and noisy or
less contrasting backgrounds. Hence, fingerprint enhancement plays a vital role
in the early stages of the fingerprint recognition/verification pipeline. In
this paper, we investigate and improvise the encoder-decoder style architecture
and suggest intuitive modifications to U-Net to enhance low-quality
fingerprints effectively. We investigate the use of Discrete Wavelet Transform
(DWT) for fingerprint enhancement and use a wavelet attention module instead of
max pooling which proves advantageous for our task. Moreover, we replace
regular convolutions with depthwise separable convolutions, which significantly
reduces the memory footprint of the model without degrading the performance. We
also demonstrate that incorporating domain knowledge with fingerprint minutiae
prediction task can improve fingerprint reconstruction through multi-task
learning. Furthermore, we also integrate the orientation estimation task to
propagate the knowledge of ridge orientations to enhance the performance
further. We present the experimental results and evaluate our model on FVC 2002
and NIST SD302 databases to show the effectiveness of our approach compared to
previous works.