Fingerprint pores extractor

Automatic Fingerprint Recognition Systems (AFRSs) rely on minutiae position and orientation within the fingerprint image for matching. Minutiae information is highly accurate provided that the fingerprint image matched is of high quality. However, this is not always the case because of diseases and hash working conditions that affect fingerprints. In order to maintain high level of security independent of varying fingerprint image quality research suggests the use of other fingerprint features to compliment minutiae. These are things like ridge contours, sweat pores, dots, and incipient ridges. Sweat pores have been proven as one of the most distinctive among these feature. Thus in order to improve accuracy of AFRSs pores can be fused with minutiae or used alone. Sweat pores have been less utilized in the past due to constraints imposed by fingerprint scanning devices and resolution standards. Recently, progress has been made on both scanning devices and resolution standards to support the use of pores in AFRSs. However, very few techniques exist for extracting, matching and fusing them with minutiae. Matching and fusion can only be possible if pores are available. Some techniques have been proposed to reliable extract pores. However, existing techniques can only work on one resolution i.e. an algorithm proposed and tested on 500dpi cannot work on 1000dpi without minor modifications because pores size change if resolution changes. In addition, existing pore extraction techniques are computationally expensive. In this paper an algorithm to extract feature level 3 (pores) is proposed. The algorithm uses Laplacian of Gaussian (LoG) in Fourier domain in order to reduce computation. The performance of the proposed algorithm is tested on two distinct databases with different resolutions in order to validate its accuracy. The accuracy of the proposed algorithm is further measured using false detection rate (FDR) and true detection rate (TDR). Results show that FDR ranges from 10-35% while TDR ranges from 65-90%.