A Framework for Fingerprint Liveness Detection Using Support Vector Machine Optimized by Genetic Algorithm

Volume 5 Issue 2 June - August 2018

Research Paper

A Framework for Fingerprint Liveness Detection Using Support Vector Machine Optimized by Genetic Algorithm

Yusuf Ibrahim*, Muhammed B. Mu’azu**, Emmanuel A. Adedokun***, Yusuf A. Sha’aban****

*,**** Lecturer, Department of Computer Engineering, Ahmadu Bello University, Zaria, Nigeria.

** Professor and Head, Department of Computer Engineering, Ahmadu Bello University, Zaria, Nigeria.

*** Department of Electrical Engineering, Ahmadu Bello University, Zaria, Nigeria.

Ibrahim, Y., Mu’azu, M. B., Adedokun, E. A.,and Sha’aban, Y. A (2018). A Framework for Fingerprint Liveness Detection Using Support Vector Machine Optimized by Genetic Algorithm. i-manager’s Journal on Pattern Recognition, 5(2), 1-9. https://doi.org/10.26634/jpr.5.2.15536

Abstract

Fingerprints are widely and successfully been used in a number of applications as a preferred biometric for personal identifications. However, current fingerprint authentication systems are vulnerable to direct spoof attacks at the sensor level as fake fingerprints artificially made to replicate genuine ones are now made using common materials such as silicone, gelatin, playdoh etc. This paper therefore implements a software based deep machine learning framework for classifying fingerprints images presented to the system as either been live or fake. Since typical fingerprint images are noisy, some preprocessing on the images were first of all performed using a decision based adaptive median filtering algorithm for de-noising and min-max normalization for enhancement. Features were then extracted using pre- trained Deep Convolutional Neural Network (DCNN) and their dimensionality reduced using Principal Component Analysis (PCA). Resulting features were then used to train a Support Vector Machine with Gaussian kernel optimized by Genetic Algorithm. The developed GA-SVM method was evaluated on the 3993 Biometrika datasets from the LivDet2009 database. The results obtained demonstrate robustness and effectiveness of the developed method in achieving good average liveness classification accuracy.