Fingerprint card classification with statistical feature integration

Uchida, Kaoru; Kamei, Toshio; Mizoguchi, Masanori; Temma, T.

doi:10.1109/icpr.1998.712087

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…In a fingerprint image, for each ridge ending, there is generally a corresponding valley bifurcation and vice versa [2], with the only exception at the singularity points (cores and deltas) [14]. This is called the termination/bifurcation duality, as illustrated in Fig.…”

Section: Postprocessing Algorithmsmentioning

confidence: 99%

Preprocessing and postprocessing for skeleton-based fingerprint minutiae extraction

Zhang

Tang

2007

Pattern Recognition

120

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Section: Postprocessing Algorithmsmentioning

confidence: 99%

Preprocessing and postprocessing for skeleton-based fingerprint minutiae extraction

Zhang

Tang

2007

Pattern Recognition

120

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“…Existing fingerprint database filtering techniques are based on pattern classification [2], relationships among level 1 features [8], orientation information around reference points [5] and relationships among level 2 features [3]. Due to limited information contained in latent fingerprints, a single filtering technique may not be adequate.…”

Section: Introductionmentioning

confidence: 99%

“…A multi-stage filtering system is proposed, which utilizes pattern type, singular points and orientation field. We have tested our system by searching 258 latent fingerprints in NIST SD27 against a background database containing 10,258 rolled fingerprints (obtained by combining 2,000 in NIST SD4,8,000 in SD14 and 258 in SD27). Although latent fingerprints contain very limited information, the filtering system not only improved the matching speed by three fold but also improved the rank-1 matching accuracy from 70.9% to 73.3%.…”

mentioning

confidence: 99%

Filtering large fingerprint database for latent matching

Feng

Jain

2008

2008 19th International Conference on Pattern Recognition

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Latent fingerprint identification is of critical importance to law enforcement agencies in apprehending criminals. Considering the huge size of fingerprint databases maintained by law enforcement agencies, exhaustive one-to-one matching is impractical and a database filtering technique is necessary to reduce the search space. Due to low image quality and small finger area of latent fingerprints, it is necessary to use several features for an efficient and reliable filtering system. A multi-stage filtering system is proposed, which utilizes pattern type, singular points and orientation field. We have tested our system by searching 258 latent fingerprints in NIST SD27 against a background database containing 10,258 rolled fingerprints (obtained by combining 2,000 in NIST SD4,8,000 in SD14 and 258 in SD27). Although latent fingerprints contain very limited information, the filtering system not only improved the matching speed by three fold but also improved the rank-1 matching accuracy from 70.9% to 73.3%.

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