Comprehensive edge direction descriptor for fingerprint liveness detection

The edge direction is one of the most discriminative image information. The existing edge direction based feature extraction methods only extract the explicit edge direction information but ignore the complementary implicit edge direction information. In this paper, we propose a comprehensive edge direction information based image feature extraction method for fingerprint liveness detection. The main novelties of our method can be summarized as follows. (1) We propose the singular value decomposition (SVD) based image Log-Gabor transform energy extraction method, with the energy image preserving the most dominant convolution response. (2) We extract the explicit and implicit edge direction information from the Log-Gabor transform of original image and SVD energy image respectively. (3) Adopting the Log-Gabor filters as the codewords, we propose the orientation normalization based scale Non-K-Maximum suppression Log-Gabor transform encoding method and the positive–negative separation mean pooling method. Our method is of lower computational complexity compared with existing codeword based image representation methods. Extensive experimental evaluations on three benchmark databases show that the proposed method yields desirable performance compared with the state-of-the-art methods.