Automatic segmentation of latent fingerprints - Choi, Heeseung; Boaventura, Maurilio [UNESP]; Boaventura, Ines A. G. [UNESP]; Jain, Anil K. (Institute of Electrical and Electronics Engineers (IEEE), 2012)
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. © 2012 IEEE.
Keywords: Automatic segmentations; Crime scenes; Feature types; Fingerprint ridges; Frequency features; Ground truth; Latent fingerprint; Law-enforcement agencies; Matching performance; Orientation tensor; Random noise; Region of interest; Ridge frequency; Ridge orientations; Ridge patterns; Segmentation algorithms; Segmentation results; Segmented regions; Symmetric patterns; Biometrics; Crime; Fourier analysis; Image segmentation