Explainable Fingerprint ROI Segmentation Using Monte Carlo Dropout

摘要

A fingerprint Region of Interest (ROI) segmentation module is one of the most crucial components in the fingerprint pre-processing pipeline. It separates the foreground finger-print and background region due to which feature extraction and matching is restricted to ROI instead of entire finger-print image. However, state-of-the-art segmentation algorithms act like a black box and do not indicate model confidence. In this direction, we propose an explainable finger-print ROI segmentation model which indicates the pixels on which the model is uncertain. Towards this, we benchmark four state-of-the-art models for semantic segmentation on fingerprint ROI segmentation. Furthermore, we demonstrate the effectiveness of model uncertainty as an attention mechanism to improve the segmentation performance of the best performing model. Experiments on publicly available Fingerprint Verification Challenge (FVC) databases show-case the effectiveness of the proposed model.