FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-Net Based Convolutional Neural Networks Sukesh Adiga V and Jayanthi Sivaswamy Center for Visual Information Technology (CVIT), IIIT-Hyderabad, India sukesh.adigav@research.iiit.ac.in Abstract. Fingerprint is a common biometric used for authentication and verification of an individual. These images are degraded when fingers are wet, dirty, dry or wounded and due to the failure of the sensors, etc. The extraction of the fingerprint from a degraded image requires denois- ing and inpainting. We propose to address these problems with an end- to-end trainable Convolutional Neural Network based architecture called FPD-M-net, by posing the fingerprint denoising and inpainting problem as a segmentation (foreground) task. Our architecture is based on the M-net with a change: structure similarity loss function, used for better extraction of the fingerprint from the noisy background. Our method outperforms the baseline method and achieves an overall 3rd rank in the Chalearn LAP Inpainting Competition Track 3-Fingerprint Denoising and Inpainting, ECCV 2018. Keywords: Fingerprint image, Denoising, Inpainting, Deep learning. 1 Introduction Fingerprint is an impression left by friction ridges of a finger. Human finger- prints are detailed, nearly unique, difficult to alter, and durable over the life of an individual, making them suitable as long-term biometrics for identifying the uniqueness of an individual. It plays an increasingly important role in security, to ensure privacy and identity verification. Fingerprint-based authentication is ubiquitous in day to day life (Example: unlocking in smartphones, mobile pay- ments, international travel, accessing the restricted area, etc.). In forensic appli- cations, the accuracy of fingerprint retrieval and verification systems are critical. However, recovery of fingerprints deposited on surfaces such as glass or metal or polished stone remains challenging. Fingerprints details can be degraded due to impression conditions such as humidity, wet, dirty, skin dryness, and non-uniform contact with fingerprint capture device [7]. This results in poor image quality, hence require a denoising fingerprint information from the noise. In some cases, image can have missing regions due to the failure of fingerprint sensors or wound in finger. It requires a filling or inpainting from the neighbouring region. Overall, fingerprint image arXiv:1812.10191v2 [cs.CV] 22 Mar 2019