Abstract
Fingerprint-based recognition systems have been widely deployed in numerous civilian and government applications. However, the fingerprint recognition systems can be deceived by commonly used sensors with the artificially fake fingerprint made using materials like gelatin or silicon. In this paper, spoof fingerprint detection is considered as a two-class classification problem and co-occurrence matrix is constructed from image gradients to extract features. In feature extraction process, the quantization operation is firstly applied with the fingerprint images. Secondly, the horizontal and vertical differences at each pixel are calculated. Thirdly, the differences of large absolute values are truncated into a reduced range. Finally, the co-occurrence matrix is constructed from the truncated differences, and the elements of the co-occurrence matrix are directly used as features. The features are separately utilized to train support vector machine classifiers on two databases. The experimental results have demonstrated that the proposed method outperform the state-of-the-arts.
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