基于韦伯二值感知特征的指纹活性检测

doi:10.3969/j.issn.0255-8297.2016.05.014

Abstract

Abstract:

Based on Weber binarized perception features (WBPF), we propose a fingerprint liveness detection method. It consists of two components: local binary differential excitation (LBDE) for extracting perception features by Weber's law, local binary gradient orientation (LBGO) for extracting gradient based orientation features. The features are used to train SVM classifiers on two publicly available databases used in the Fingerprint Liveness Detection Competition 2011 and 2013. Experimental results show that the proposed method outperforms the state-of-the-art liveness detection techniques.

Key words: biometrics, fingerprint liveness detection, local descriptor, feature extract, Weber's law

CLC Number:

TN391

LÜ Rui, XIA Zhi-hua, CHEN Xian-yi, SUN Xing-ming. Fingerprint Liveness Detection Based on Weber Binarized Perception Features[J]. Journal of Applied Sciences, 2016, 34(5): 616-624.

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Fingerprint Liveness Detection Based on Weber Binarized Perception Features

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