Automatic segmentation of latent fingerprints

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorChoi, Heeseung
dc.creatorBoaventura, Maurilio
dc.creatorBoaventura, Ines A. G.
dc.creatorJain, Anil K.
dc.date2014-05-27T11:27:17Z
dc.date2016-10-25T18:39:59Z
dc.date2014-05-27T11:27:17Z
dc.date2016-10-25T18:39:59Z
dc.date2012-12-01
dc.date.accessioned2017-04-06T02:03:54Z
dc.date.available2017-04-06T02:03:54Z
dc.identifier2012 IEEE 5th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2012, p. 303-310.
dc.identifierhttp://hdl.handle.net/11449/73800
dc.identifierhttp://acervodigital.unesp.br/handle/11449/73800
dc.identifier10.1109/BTAS.2012.6374593
dc.identifier2-s2.0-84872000216
dc.identifierhttp://dx.doi.org/10.1109/BTAS.2012.6374593
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/894585
dc.descriptionLatent 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.
dc.languageeng
dc.relation2012 IEEE 5th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2012
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAutomatic segmentations
dc.subjectCrime scenes
dc.subjectFeature types
dc.subjectFingerprint ridges
dc.subjectFrequency features
dc.subjectGround truth
dc.subjectLatent fingerprint
dc.subjectLaw-enforcement agencies
dc.subjectMatching performance
dc.subjectOrientation tensor
dc.subjectRandom noise
dc.subjectRegion of interest
dc.subjectRidge frequency
dc.subjectRidge orientations
dc.subjectRidge patterns
dc.subjectSegmentation algorithms
dc.subjectSegmentation results
dc.subjectSegmented regions
dc.subjectSymmetric patterns
dc.subjectBiometrics
dc.subjectCrime
dc.subjectFourier analysis
dc.subjectImage segmentation
dc.titleAutomatic segmentation of latent fingerprints
dc.typeOtro


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