Open AccessEffective statistical‐based and dynamic fingerprint preprocessing technique
Author(s) -
Iloanusi Ogechukwu N.
Publication year - 2017
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
eISSN - 2047-4946
pISSN - 2047-4938
DOI - 10.1049/iet-bmt.2015.0064
Subject(s) - computer science , preprocessor , biometrics , fingerprint (computing) , thresholding , artificial intelligence , orientation (vector space) , pattern recognition (psychology) , matching (statistics) , fingerprint recognition , field (mathematics) , data pre processing , word error rate , noise (video) , data mining , computer vision , image (mathematics) , mathematics , statistics , geometry , pure mathematics
Challenges to contextual filtering techniques include difficulties in estimating orientation field in poor quality images and subsequently failure to extract ridges reliably in large regions of low quality. This study proposes a statistical‐based and dynamic fingerprint preprocessing technique for adaptive contrast enhancement and binarisation of fair and poor qualities plain and rolled fingerprints with large regions of low quality, prior to orientation field estimation. The algorithm effectively enhances smudged and faded ridges uniformly in recoverable regions, based on values of statistical variables computed locally in each region. The preprocessing algorithm employs a locally adaptive thresholding approach resulting in enhanced binarised images. The performance of the proposed algorithm was determined by carrying out biometric verification evaluation using a popular commercial biometric matching software, on databases of fingerprints in their original forms, as well as same fingerprints enhanced with the proposed algorithm. Experiments show that fingerprints are uniformly enhanced and binarised; and smudged or faded ridges in recoverable regions made visible. Fingerprint verification evaluation on preprocessed fingerprints resulted in lower error rates in 12 databases. These results show that the proposed algorithm significantly improves recognition.