Finger texture biometric verification exploiting Multi-scale Sobel Angles Local Binary Pattern features and score-based fusion

R. R.O. Al-Nima, M. A.M. Abdullah, M. T.S. Al-Kaltakchi, Satnam Dlay, W. L. Woo, J. A. Chambers

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

In this paper a new feature extraction method called Multi-scale Sobel Angles Local Binary Pattern (MSALBP) is proposed for application in personal verification using biometric Finger Texture (FT) patterns. This method combines Sobel direction angles with the Multi-Scale Local Binary Pattern (MSLBP). The resulting characteristics are formed into non-overlapping blocks and statistical calculations are implemented to form a texture vector as an input to an Artificial Neural Network (ANN). A Probabilistic Neural Network (PNN) is applied as a multi-classifier to perform the verification. In addition, an innovative method for FT fusion based on individual finger contributions is suggested. This method is considered as a multi-object verification, where a finger fusion method named the Finger Contribution Fusion Neural Network (FCFNN) is employed for the five fingers. Two databases have been employed in this paper: PolyU3D2D and Spectral 460 nm (S460) from CASIA Multi-Spectral (CASIA-MS) images. The MSALBP feature extraction method has been examined and compared with different Local Binary Pattern (LBP) types; in classification it yields the lowest Equal Error Rate (EER) of 0.68% and 2% for PolyU3D2D and CASIA-MS (S460) databases, respectively. Moreover, the experimental results revealed that our proposed finger fusion method achieved superior performance for the PolyU3D2D database with an EER of 0.23% and consistent performance for the CASIA-MS (S460) database with an EER of 2%.
Original languageEnglish
Pages (from-to)178-189
Number of pages12
JournalDigital Signal Processing: A Review Journal
Volume70
Early online date18 Aug 2017
DOIs
Publication statusPublished - Nov 2017

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