Localized Dictionaries Based Orientation Field Estimation for Latent Fingerprints
Abstract:
Fingerprint matching is challenging, as the matcher has to minimize two competing error rates: the False Accept Rate and the False Reject Rate. A fingerprint is the impression made by the papillary ridges on the ends of the fingers and thumbs. Fingerprints afford an infallible means of personal identification, because the ridge arrangement on every finger of every human being is unique and does not alter with growth or age. Fingerprints serve to reveal an individual's true identity despite personal denial, assumed names, or changes in personal appearance resulting from age, disease, plastic surgery, or accident. Dictionary based orientation field estimation approach has shown promising performance for latent fingerprints. In this paper, we seek to exploit stronger prior knowledge of fingerprints in order to further improve the performance. we propose a localized dictionaries-based orientation field estimation algorithm, in which noisy orientation patch at a location output by a local estimation approach is replaced by real orientation patch in the local dictionary at the same location.We propose a Hough transform-based fingerprint pose estimation algorithm, in which the predictions about fingerprint pose made by all orientation patches in the latent fingerprint are accumulated. By using these techniques the efficiency will be increased more when compared to the existing system.
Existing System:
In the Existing system,The fingerprint will be accessing the template which was stored in the database. With that the efficiency for the authentication will be low. Hough transform-based fingerprint pose estimation algorithm, in which the predictions about fingerprint pose made by all orientation patches in the latent fingerprint are accumulated.
Disadvantages:
1) The Existing System only used an image based approach.
2) This system does not support the minutia approach
3) This system also takes long time identification.
4) This system result should not accurate.
Proposed System:
In our method the ridge features and conventional minutiae features(Minutiae type, orientation, and position). The types of proposed fingerprints recognition are an image. Enhancement method should have three important properties. Reconnect broken ridges, e.g., caused by dryness of the finger Or scars. Separate falsely conglutinated ridges, e.g., caused by wetness Of the finger or smudges. Preserve ridge endings and bifurcations. Ridge bifurcation will be done when estimating the fingerprint of an individual. Maintaining the structure of the ridges even when we are missing some part of the fingerprint. Ridge features are composed of four elements: ridge count, ridge Length, ridge curvature direction, and ridge type.
Advantages:
1. It present the construction of training orientation fields, then describe to obtain the prototype orientation patches and to learn their spatial distributions, and finally present the probabilistic voting algorithm for pose estimation.
2. Training set by manually marking the orientation fields and the pose of many fingerprints in a public domain database.
Software Requirements:
Platform : JDK 1.5
Program Language : JAVA
IDE : Net Beans 6.9
Data Base : My sql
Operating System : Microsoft Windows XP
Hardware Requirements:
Processor : Pentium IV Processor
RAM : 512 MB
Hard Drive : 10GB
Monitor : 14” VGA COLOR MONITOR
USB Finger Print Reader : SecuGen Hamster Plus