Gabor Filter-Based Fingerprint Anti-spoofing
This paper describes Gabor filter-based method to detect spoof fingerprint attacks in fingerprint biometric systems. It is based on the observation that, real and spoof fingerprints exhibit different textural characteristics. Textural measures based on Gabor energy and co-occurrence texture features are used to characterize fingerprint texture. Fingerprint image is filtered using a bank of four Gabor filters, and then a gray level co-occurrence matrix (GLCM) method is applied to filtered images to extract minute textural details. Dimensionality of the features is reduced by principal component analysis (PCA). We test features on three different classifiers: neural network, support vector machine and OneR; then we fuse all the classifiers using the “Max Rule” to form a hybrid classifier. Overall classification rates achieved with various classifiers range from ~94.12% to ~97.65%. Thus, the experimental results indicate that, the new liveness detection approach is a very promising technique, as it needs only one fingerprint and no extra hardware to detect vitality.
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- 5.Tan, B., Schuckers, S.: Liveness detection for fingerprint scanners based on statistics of wavelet signal processing. In: Proceedings of Computer Vision and Pattern Recognition Workshop (CVPRW) (2006)Google Scholar
- 10.Smith, J., Chang: Transform features for texture classification and discrimination in large image databases. In: Proceedings of IEEE International Conference on Image Processing, vol. 3, pp. 407–411 (November 1994)Google Scholar
- 15.Vapnik, V.N.: The nature of statistical learning theory (Information science and statistics). Springer, Heidelberg (1999)Google Scholar
- 16.LIBSVM – A Library for Support Vector machine (SVM) Classifier, http://www.csie.ntu.edu.tw/~cjlin/libsvm/
- 20.Polikar, R.: Ensemble based systems in decision making- Feature article. IEEE Circuits and Systems Magazine, Third quarter, 21–45 (2006)Google Scholar