Gabor Filter-Based Fingerprint Anti-spoofing

  • Shankar Bhausaheb Nikam
  • Suneeta Agarwal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5259)


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.


Support Vector Machine Gabor Filter Fingerprint Image Gabor Feature Core Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Shankar Bhausaheb Nikam
    • 1
  • Suneeta Agarwal
    • 1
  1. 1.Department of Computer Science and EngineeringMotilal Nehru National Institute of TechnologyAllahabadIndia

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