Fingerprint liveness detection based on binarized statistical image feature with sampling from Gaussian distribution

Fingerprint detection has been applied in many security applications due to its satisfying performance. Its popularity also attract the attack from an adversary who uses artificial fingerprint made of Play-Doh or silicon to mislead the decision of the system. Recently, the liveness fingerprint detection using the textural measures based on the descriptor named Binarized Statistical Image Features (BSIF). BSIF is calculated for every pixel in a fingerprint image. However, we believe that the pixels in the middle of the fingerprint contain more information than the ones in the edge. As a result, we propose a revised liveness fingerprint detection based on BSIF with sampling from Gaussian distribution. According to the Gaussian distribution, more pixels in the middle will be sampled in comparison with the ones at the edge. The experimental results show that the proposed method is more accurate than the traditional one. It may suggests that the pixels in the middle are more informative than the ones at the edge for liveness detection.