Latent Fingerprint Preprocessing: Orientation Field Correction & Enhancements

What is Latent Fingerprint?

Chance impressions, or what is more commonly known as latent fingerprints, are the oftentimes invisible patterns made by fingerprints that are usually left at crime investigations or on objects recovered from crime scenes, and forensically analyzed by latent fingerprint experts with the application of chemical or physical methods.

Abstract:

Latent Fingerprint Images have been extensively used by law enforcement agencies in investigating the crime spot and use the necessary information obtained as evidence to validate the criminal in Court. Although an important breakthrough in this direction has already been made in plain biometrics recognition, still identifying biometric such as Face in CCTV footage and Latent Fingerprint in uncontrolled, uncooperative, and hostile environment is still an open research problem. Poor quality, lack of clarity, absence of proper mechanism make the latent fingerprint preprocessing problem one of the persistent and challenging problem to extract the reliable features.

Dictionary based learning technique has given significant result, in contrast to conventional orientation field estimation methods by reconstructing orientation field to enhance the latent fingerprint image. Distorted orientation field is corrected using orientation patches of good quality fingerprint from a region wise dictionary. This project proposes a fresh idea to construct the dictionary by region wise to correct the orientation field in a latent image.

Index-terms: Fingerprint matching, fingerprint enhancement, latent fingerprint, orientation field, Region Wise dictionary.

Important Links & related reads:

  1. Encyclopedia
  2. Fingerprint Analysis
  3. Orientation Field
  4. Biometric Research Group.
  5. How fingerprinting improves criminal invertigations.
  6. MATLAB Tutorial.

Related Papers:

  1. http://biometrics.cse.msu.edu/pubs/fingerprints.html

Fingerprint databases used in this study:

  1. NIST Special Database 4.

Contribution

  • Report issues How to?
  • Open pull request with improvements How to?
  • Spread the word.

Contacts:

  1. Shankar Lal Agarwal (shankar892011@gmail.com)
  2. Anick Saha (anick1343.11@bitmesra.ac.in)
  3. Dr.Indrajit Mukherjee (imukherjee@bitmesra.ac.in)