Separating Overlapped Fingerprints

Chen, Fanglin; Feng, Jianjiang; Jain, Anil K.; Zhou, Jie; Zhang, Jin

doi:10.1109/tifs.2011.2114345

Cited by 58 publications

(46 citation statements)

References 21 publications

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“…Such overlapped fingerprint images, while difficult to process, are useful forensic evidence for identifying suspects. Available fingerprint matchers, however, can not accurately match overlapping fingerprints, because they assume that a fingerprint image contains only a single fingerprint and hence single orientation field [2]. Our interest here is to develop algorithms to separate overlapping latents that will serve as a valuable tool in forensics.…”

Section: Introductionmentioning

confidence: 99%

“…Manually marking minutiae in overlapped fingerprint images is not only tedious, but also very difficult. Recent publications [2], [4]- [7] have proposed algorithms to separate two overlapping fingerprints with minimal markup. Given the component fingerprints, latent examiners no longer need to mark the minutiae since the component fingerprints can be effectively matched by commercial matchers.…”

Section: Introductionmentioning

confidence: 99%

“…Given the component fingerprints, latent examiners no longer need to mark the minutiae since the component fingerprints can be effectively matched by commercial matchers. Chen et al [2] and Shi et al [4] showed an improvement in the matching accuracy of component fingerprints compared to the overlapping print by applying a relaxation labeling based algorithm on relatively good quality overlapped images synthesized from the FVC2002 Db1 b database [8]. Figure 2 shows an example of synthesized overlapped fingerprint image used in [2] and [4].…”

Section: Introductionmentioning

confidence: 99%

“…Chen et al [2] and Shi et al [4] showed an improvement in the matching accuracy of component fingerprints compared to the overlapping print by applying a relaxation labeling based algorithm on relatively good quality overlapped images synthesized from the FVC2002 Db1 b database [8]. Figure 2 shows an example of synthesized overlapped fingerprint image used in [2] and [4]. State-of-the-art overlapping fingerprints separation algorithms [2], [4] consist of three main steps.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 2 shows an example of synthesized overlapped fingerprint image used in [2] and [4]. State-of-the-art overlapping fingerprints separation algorithms [2], [4] consist of three main steps. First, orientation field is estimated from the overlapped fingerprint image by local Fourier analysis.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Model Based Separation of Overlapping Latent Fingerprints

Zhao

Jain

2012

IEEE Trans.Inform.Forensic Secur.

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Abstract-Latent fingerprints lifted from crime scenes often contain overlapping prints, which are difficult to separate and match by state-of-the-art fingerprint matchers. A few methods have been proposed to separate overlapping fingerprints to enable fingerprint matchers to successfully match the component fingerprints. These methods are limited by the accuracy of the estimated orientation field, which is not reliable for poor quality overlapping latent fingerprints. In this paper, we improve the robustness of overlapping fingerprints separation, particularly for low quality images. Our algorithm reconstructs the orientation fields of component prints by modeling fingerprint orientation fields. In order to facilitate this, we utilize the orientation cues of component fingerprints, which are manually marked by fingerprint examiners. This additional markup is acceptable in forensics, where the first priority is to improve the latent matching accuracy. The effectiveness of the proposed method has been evaluated not only on simulated overlapping prints, but also on real overlapped latent fingerprint images. Compared with available methods, the proposed algorithm is more effective in separating poor quality overlapping fingerprints and enhancing the matching accuracy of overlapping fingerprints.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%