Explainable Fingerprint ROI Segmentation Using Monte Carlo Dropout

Indu Joshi, Riya Kothari, Ayush Utkarsh, Vinod K. Kurmi, Antitza Dantcheva, Sumantra Dutta Roy, Prem Kumar Kalra; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2021, pp. 60-69

A fingerprint ROI segmentation module is one of the most crucial component in the fingerprint pre-processing pipeline. It separates the foreground fingerprint and background region due to which feature extraction and matching is restricted to ROI instead of entire fingerprint 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 fingerprint 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 showcase the effectiveness of the proposed model.