Fingerprint Recognition

ABSTRACT:

Fingerprint image analysis for automatic identification technology has been developed for use in a number of major applications. Important industries affected by this technology include network security and protection, smart money, ATM transaction, and biometric identifier systems for many major government sectors. In this paper we discuss the major components of the technology including the live-scan fingerprint subsystem, the WSQ compression algorithm, and the recognition algorithm.

INTRODUCTION:

The fingerprints have been used as a mean for identifying individual for a long time because the fingerprints are unique and stay unchanged through out an individual life time. The chance of two people—even identical twins—having the same fingerprint is probably less than one in a billion. Fingerprint comparison is the most widely used method of biometric authentication and the most cost effective. Currently there are about 200 million FBI cards (10 fingerprints per cards) stored in cabinets that would fill an area of one acre. The manual effort of identifying and maintaining such a system is very cumbersome, time consuming and expensive as the number of finger print records grows at a rate from 30 to 50 thousands cards per day [1]. With the advancement of computer technology the problem of automatic finger print identification has attracted wide attention among researchers that results in automatic fingerprint identification system (AFIS) available today. Going in hands with the recognition problem is the problem of real-time matching system for large fingerprint data bases. Since the storage requirement for such a large amount of data can be thousands of terabytes system, data compression is another aspect of automatic identification using fingerprints. Currently the FBI data compression specification for finger is the “de facto national standard which is based on wavelet transform scalar quantization (WSQ)”.




AFIS: AUTOMATIC FINGERPRINT IDENTIFICATION SYSTEM

The four main components of an AFIS system is the scanner, the recognition algorithm, the search and query algorithm of the data base and the data compression algorithm.

1. The Live Scanner:
The live scanner captures the finger print at a minimum resolution of 500 pixels per inch in both row and column and each pixel shall be gray level quantized to 8 bits. Regardless of the method and media used by the scanner, the electronic image must be sufficient quality to provide conclusive finger print comparison, successful finger classification and feature detection, and can support an AFIS search reliably. The major consideration for the scanner is whether or not it meets number test procedures that will warranty the image quality as stated in the Minimum Image Quality Requirement, Electronically Produced, Fingerprint Cards, and Appendix F- IAFIS Image Quality Specifications.

¨ Geometric Image Accuracy
§ 1% for distance between .07 and 1.5 inch
§ .0007 for distance less than or equal to .07 inch

¨ Modulation Transfer Function (MTF).
MTF is the point response of the image capturing system. For each frequency the Image Modulation (IM) is computed.
IM = (Max- Min)/ (Min-Max)
The MTF is then computed by dividing the Image Modulation by the Target Modulation.




¨ Signal to Noise Ratio (SNR).
For adequate image quality, the SNR must be less than 125 for both black and white noise.
The SNR is computed as the difference between the average white and the average black value, alternately divided by the white noise standard deviation and the black noise standard deviation.
¨ Grey-Scale Range of Image Data
At least 80% of the captured images should have a dynamic range of at least 200 grey levels and at least 90% shall have a dynamic range of at least 128 grey levels.
¨ Grey Scale Linearity and Grey Level Uniformity
Linearity and uniformity of the grey level must meet a standard to assure an image quality suitable for AFIS. When scanning a uniform reference of white (and black), no two adjacent rows or columns of length 5 pixels or greater shall have an average grey scale different more than a threshold value, pixel’s grey level must remain within a deviation from mean value of local area, the mean grey level of adjacent quarter inch area shall not differ by certain value.

2. Fingerprint Matching:

The fingerprint matching process can be represented by the flowing block diagram
Matching block diagram



The pre-processing aim is to improve the quality of the image. The pre-processing has two tasks:
¨ Ridge enhancement
¨ Restoration and segmentation of fingerprint image
The pre-processing step produces a binary segmented fingerprint ridge image from an input grey scale image. The ridges have a value of ‘1’ and the rest of the image has value of ‘0’. The pre-processing steps involve
¨ Computation of orientation field
¨ foreground/background separation,
¨ ridge segmentation ,
¨ Directional smoothing of ridge
Analysis of the fingerprints shows that the ridges exhibit different anomalies referred to as ridge ending, ridge bifurcation, short ridge, ridge crossovers etc... There are some eighteen different types of features enumerated and called minutiaes. The most frequently used are the ridge ending and ridge bifurcations.


(a): Ridge ending (b): Ridge bifurcation (c): Ridge direction

A typical good finger print has about 70 to 80 minutiae points. Other complex fingerprint features can be expressed as a combination of these two features. The features are normally recorded as a vector with three attributes: the x-co-ordinate, the y-co-ordinate, and the local ridge direction ().
The finger matching is the matching of the minutiae sets. This can be done with number of techniques including point set matching, graph matching, and sub-graph isomorphism.
3. Fingerprint classification:
























Block Diagram of Classification Algorithm

Given the database for the fingerprints is very large, the matching should be done only on a subset of the database. To this end, the fingerprints are classified in to five main categories as high-level features that can be used in reducing the search source during a match. They are: arch, tented arch, left loop, right loop, and whorl. The singular points commonly used are the core and the delta. The core is the highest point on the innermost ridge; the delta is the point at which three ridges radiated from it.

III. The Wavelet Scalar Quantization:

The US Federal Bureau of Investigation (FBI) has formulated a national standard for digitization and compression of grey-scale fingerprint image. At a 15:1 compression ration, the WSQ is a lossy compression but can produce archival-quality image. The compression and decompression is based on adaptive uniform scalar quantization of discrete wavelet transform sub band decomposition.



WSQ Decoder
The encoding consists of three main processes:
¨ The discrete wavelet transform (DWT) decomposition,
¨ The scalar quantization, and
¨ The Huffman entropy coding.







In the DWT step, the digital image is split into 64 spatial frequency sub bands by a two-dimensional discrete wavelet transform which is a multi rate digital filter bank. The output DWT coefficient which is in floating point arithmetic format is truncated by the scalar quantization step ("quantized"). The integer indices output by the quantization encoder are entropy-encoded by run-length coding of zeros and Huffman coding. The compressed image data, a table of wavelet transform specifications, tables for the scalar quantizes and the Huffman codes are concatenated into a single bit stream of compressed data.
In the WSQ, a two-dimensional symmetric wavelet transform is applied to the input image by transforming first the rows and then the column yielding four-channel decomposition. The four sub bands are then cascaded back through the two-dimensional analysis bank to produce more refined sixteen-bank decomposition. This process is repeated until 64-band decomposition is achieved.
The WSQ decoder reverses the process above to reproduce the finger print image from compressed data. The Huffman decoder first recovers the quantized DWT coefficients. Through the de quantizer, approximation of the original floating point format of the DWT coefficients obtained and the coefficients are feed to an inverse DWT to reconstruct the finger print image.












Application: The Conceptual Design of a Fingerprint based Identifier

¨ Verification of driver-license authenticity and license validity check

Verifying the matching between driver fingerprint and the fingerprint features stored on the license assures that the driver is indeed the person that the license is issued for. This task can be done on-site where the fingerprint features obtained from the driver by live scanning is compared with the features magnetically stored on the driver license. Current "smart card" technology allows abundant memory capacity to store the features on card.A driver/ license match means that the license indeed belongs to the driver, this, however does no warranty that the driver license is not falsified. To check for validity of the driver license the police officer has the option to make additional inquiry against the database.
In this case license validity check will result.









CONCLUSION:

We have presented the overview of the finger print technology which include primarily the scanner, the classification of fingerprint image in the database, the matching algorithms and the compression\decompression algorithm standardized by the FBI. Certain standard perhaps might be needed for this area before major commercial system applications can be implemented. An application which is a part of the fingerprint based biometric systems for commercial driver license has been shown. Once the standards and compliance procedures are in place, one can predict an explosion in the number of applications of the fingerprint technology to important industries including network security and protection, smart money, ATM transaction, military installations , airports and other secure facilities.