Integration of Statistical Method and Zernike Moment as Feature Extraction in Liveness Detection

Joint Conference on Green Engineering Technology & Applied Computing 2019

工业技术（总论）;计算机科学

Ahmad, A.S.^1 ; Hassan, R.^1 ; Zakaria, Z.^2 ; Ramlan, R.^3

Software Engineering Department, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bharu

81310, Malaysia^1

Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bharu

81310, Malaysia^2

Production and Operation Management Department, Faculty of Technology Management and Business, Universiti Tun Hussein Onn Malaysia, Johor, Batu Pahat

86400, Malaysia^3

关键词: Classification accuracy;    Fake fingerprints;    Feature extractor;    Fingerprint recognition systems;    Liveness detection;    Sensitivity and specificity;    Zernike moments;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/551/1/012064/pdf
DOI  :  10.1088/1757-899X/551/1/012064

来源: IOP

【 摘 要 】

Recently, fake fingerprints have been used to defeat fingerprint recognition systems. These fake fingerprints are created without the need for any expertise and use easily found materials. In this paper, a fake fingerprint detection method is proposed that employs a combination of eleven statistical methods and integrating them with Zernike Moment as the feature extractor. Based on the experimental results, the proposed method showed average classification accuracy, sensitivity and specificity of approximately 80% for all sensors used to capture fake fingerprint images fabricated by gelatine and latex materials.