  A fingerprint region of interest (roi) segmentation algorithm is designed to
separate the foreground fingerprint from the background noise. All the learning
based state-of-the-art fingerprint roi segmentation algorithms proposed in the
literature are benchmarked on scenarios when both training and testing
databases consist of fingerprint images acquired from the same sensors.
However, when testing is conducted on a different sensor, the segmentation
performance obtained is often unsatisfactory. As a result, every time a new
fingerprint sensor is used for testing, the fingerprint roi segmentation model
needs to be re-trained with the fingerprint image acquired from the new sensor
and its corresponding manually marked ROI. Manually marking fingerprint ROI is
expensive because firstly, it is time consuming and more importantly, requires
domain expertise. In order to save the human effort in generating annotations
required by state-of-the-art, we propose a fingerprint roi segmentation model
which aligns the features of fingerprint images derived from the unseen sensor
such that they are similar to the ones obtained from the fingerprints whose
ground truth roi masks are available for training. Specifically, we propose a
recurrent adversarial learning based feature alignment network that helps the
fingerprint roi segmentation model to learn sensor-invariant features.
Consequently, sensor-invariant features learnt by the proposed roi segmentation
model help it to achieve improved segmentation performance on fingerprints
acquired from the new sensor. Experiments on publicly available FVC databases
demonstrate the efficacy of the proposed work.
