Optical pattern recognition of fingerprints using distortion-invariant phase-only filter

ABSTRACT In crime detection and security verification, fmgerprint identification is extremely important. However, in the real-worldfmgerprint images are not distortion free. In the present work, a distortion-invariant identification method for fmgerprintimages is developed. The sources of distortion of fmgerprint data that are considered are rotation, scaling and erosion. It isshown that rotated fmgerprint images with 95% ofmissmg data can still be recognized. 1. INTRODUCTION From the very beginning of the civilization, personal identification is of great importance. The uniqueness and invariantnature of fmgerprint makes it an incomparable candidate for this purpose. With the increasing volume of fmgerprint traffic from law enforcement agencies and various security organizations there is an immediate need to develop automated fmgerprint recognition system. A significant number of approaches have been developed for the purpose of automaticclassification of fmgerprint over the past few decades. In the classical approaches like structural'2, statistica13, syntactical5,neural network method6, the classification of fmgerprint is accomplished by using local or global feature extraction method.These approaches suffer from various problems such as slower processing speed, higher power requirement, sensitivity tonoise, complexity in grammar rules, complicated neural nets and so on. The recent trend of fmgerprint identification ismotivated by application of optical filter and correlation to achieve high processing speed and low power requirements.A.P.Fitz and R.J Green7 used hexagonal fast Fourier transformation to classify fmgerprints into whorl, loops and arches.Kenneth H. Fielding et. al.8 used binary joint transform correlation for optical fmgerprint identification. In this approach,each fmgerprint is compared with all other fmgerprint of the database. J. Rodolfo et. a!9 also used similar approach usingphoto-refractive joint transform correlator to identify fmgerprint.Another main problem with the fmgerprint data is its distorted nature when collected form the real-world scene. Thisdistortion is generated from the image rotation and pressure variation of fmger on the sensor glass window of the inputdevices. Another source of distortion is the low reproducibility of the input device. So the real challenge is to recognize thesedistorted images.The performance of a pattern recognition (PR) system is influenced by two phenomena that may contribute to the success orfailure of a PR system. The first is the affme transform that may cause the image to change shape, simply because of thechange in the observation angle of the sensor gathering information. A second influence comes from the clutter present in thescene. A human observer may recognize an object from a different point of view, but clutter poses a real challenge to even ahuman. A phase only filter, a variation of classical matched filter (CMF), performs an edge enhancement on the picture, ittries to match the structure more than the actual gray levels, and thus it has the ability to see through the clutter and matchagainst the edges ofthe object.In this work, we established a new method of fmgerprint identification using optical correlator based phase only filter. Asimulation w ork is done to identify the effects of distortion on fmgerprint recognition reliability as well as methods tocounter those effects. In the following section we describe the simulation setup and fmgerprint database. The recognition of