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

Al-Nima, R., Abdullah, Mohammed, Al-Kaltakchi, M., Dlay, Satnam, Woo, Wai Lok and Chambers, Jonathon (2017) Finger texture biometric verification exploiting Multi-scale Sobel Angles Local Binary Pattern features and score-based fusion. Digital Signal Processing, 70. pp. 178-189. ISSN 1051-2004

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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%.

Item Type:	Article
Uncontrolled Keywords:	Finger texture, Finger fusion, Local binary pattern, Biometric verification, Probabilistic neural network
Subjects:	G900 Others in Mathematical and Computing Sciences
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Becky Skoyles
Date Deposited:	29 Mar 2019 13:21
Last Modified:	10 Oct 2019 20:48
URI:	http://nrl.northumbria.ac.uk/id/eprint/38639

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