Latent Fingerprint Algorithm

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Systems that operate with biometrics identifies a person with the behavioral and physiological biometric data. The unique characteristics such as face, fingerprint, and palm print and iris remains unchanged throughout the life of a person. The versatility of biometric system depends on the requirement of an application so that it can be used as a verification mode or identification mode. Fingerprint is a pattern of bifurcations, curves and minutiae, which are extracted using inked impression on a paper or sensors. A high quality fingerprint contains 25 to 80 minutiae depending on sensor resolution and finger placement on the sensor. In latent fingerprint matching case, it is very challenging to estimate the orientation field based only on the image due to the blurredness and lacking area of the latent. In this paper, we propose a new fingerprint matching algorithm which is especially designed for matching latent. The proposed algorithm modifies the orientation based minutia to the texture descriptor by adding the ridge frequency information and histogram equalization is also employed in improving the intensity of the latent images. The experimental results are performed on two different latent databases, NIST SD27 latent databases and the proposed algorithm is written in mat lab.

Keywords- latent,minutiae,databases-NISTSD27,orientation field
Introduction
Fingerprints remain unique for every individual and thus it plays a major role in identification of the culprits in forensics. The latent fingerprint matching algorithms proposed includes the image enhancement; feature extraction and matching of the latents.The different characteristics of the images which give a prominent difference bet...

... middle of paper ...

... latent examiners. Although this classification of latent prints as “good”, “bad”, and “ugly” is subjective, it has been shown that such a classification is correlated with the matching performance. Another indicator of fingerprint quality that affects the matching performance is the number of minutiae in the latent print.

IMPLEMENTATION

Fig: Latent Fingerprint

Fig: Thinned image

Fig: Feature Extraction

Summary

A fingerprint matching algorithm is designed for matching latents to rolled or /plain fingerprints which is based on texture based Hough transform alignment. The performance of the proposed matcher is compared for two different latent databases. The texture based descriptor is used to improve the matching accuracy especially when the overlap between the latents and rolled prints is more.

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