Automatic segmentation of latent fingerprints

Abstract

Abstract-Latent fingerprints are routinely found at crime scenes due to the inadvertent contact of the criminals' finger tips with various objects. As such, they have been used as crucial evidence for identifying and convicting criminals by law enforcement agencies. However, compared to plain and rolled prints, latent fingerprints usually have poor quality of ridge impressions with small fingerprint area, and contain large overlap between the foreground area (friction ridge pattern) and structured or random noise in the background. Accordingly, latent fingerprint segmentation is a difficult problem. In this paper, we propose a latent fingerprint segmentation algorithm whose goal is to separate the fingerprint region (region of interest) from background. Our algorithm utilizes both ridge orientation and frequency features. The orientation tensor is used to obtain the symmetric patterns of fingerprint ridge orientation, and local Fourier analysis method is used to estimate the local ridge frequency of the latent fingerprint. Candidate fingerprint (foreground) regions are obtained for each feature type; an intersection of regions from orientation and frequency features localizes the true latent fingerprint regions. To verify the viability of the proposed segmentation algorithm, we evaluated the segmentation results in two aspects: a comparison with the ground truth foreground and matching performance based on segmented region.

Latent Fingerprint Segmentation Based on Ridge Density and Orientation Consistency

“…The manual markups of ROI (Region of Interest) by experts provided in [19] are used as the ground truths for NIST SD 27 in our experiments. We compare the segmentation results of the proposed algorithm with the manual markups and other published methods [2][3][4]10]. First, we test the proposed algorithm on the same sample latent fingerprints of different image qualities from NIST SD27 database as those in [3].…”

“…Our proposed method can perform well to separate the fingerprint foreground from the complex background for latent images of different qualities, which is closer to the manual markups. Second, although the segmentation results of latent images can be evaluated visually, we compare the segmentation accuracy of our method with those of some published methods [2][3][4]10]. Two measures, the Missed Detection Rate (MDR) and the False Detection Rate (FDR), are computed for performance comparison on NIST SD27.…”

“…Short et al [9] proposed a method to apply an adaptable ridge template to determine a goodness of the fit score in a local region of fingerprint for segmentation. Choi et al [10] proposed to combine both ridge orientation and frequency features for latent fingerprint segmentation. In that method, the orientation tensor is used to obtain the symmetric patterns of fingerprint ridge orientation and the local ridge frequency is estimated by the local Fourier analysis.…”

“…Thus, it is still challenging for segmentation of latent fingerprints. There are some research efforts made to investigate new methods for improving the latent fingerprint segmentation [2][3][4][9][10][11][12][13][14][15]. Zhang et al [2] proposed an adaptive directional total variation (ADTV) model for latent fingerprint segmentation.…”

Fingerprint Segmentation Based on Adaptive and Orientation Algorithm

“…We begin with introducing the TV-L1 model, which serves as the basis for the proposed model. Similar to other TV-based image models, the TV-L1 model [4] decomposes an input image, f, into two signal layers: cartoon u, which consists of the piecewise-smooth component in f, and texture v, which contains the oscillatory or textured component in f. The decomposition: (1) is obtained by solving the following variational problem: (2) where f, u and v are functions of image gray-scale intensity values in R 2 , ∇u is the gradient value of u and λ is a constant weighting parameter. and the total variation of u and the fidelity term, respectively.…”