Open AccessSegmentation of Latent Fingerprint u sing Neural Network
Author(s) -
Neha Chaudhary,
Priti Dimri,
Harshit Singh
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a9820.109119
Subject(s) - fingerprint (computing) , artificial intelligence , segmentation , pattern recognition (psychology) , computer science , block (permutation group theory) , artificial neural network , fingerprint recognition , minutiae , computer vision , mathematics , geometry
Latent fingerprints are the fingerprints that are left by the criminal unintentionally on the surface of the crime scene. The qualities of the latent fingerprints are very poor due to the overlapping patterns and structured noises. Latent fingerprint segmentation is a difficult task due to low visibility, structured noise, and complex structure. In this paper, a fusion of morphological and neural network approach is purposed for latent fingerprint segmentation. This method automatically segments the fingerprints and non-fingerprints patterns without human intervention. The morphological method is used for segmentation of the fingerprint region. Fingerprint region then divides into y*y blocks and extracts the features of each block and uses them as an input of NN to classify the blocks into fingerprint and non-fingerprint blocks. We are using the IIIT-D database and the shows that this model batters then the existing model.