Fingerprint authentication
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14-12-2009, 07:16 PM

This is a project and implimentation based on the article published in discovery channel, on 13 may 2004 under "New invention". This is a new project and implimentation in the whole world, very useful for defense, police, bomb detectors, exploring planets etc. In this system it consists of RF transceiver. These RF transceivers are used to control the movement of the vehicle and also the othe movement
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28-02-2010, 12:40 PM


Every person is believed to have unique fingerprints. This makes fingerprint matching one of the most reliable methods for identifying people. In this diploma work shall be examined if and how the identification can be done automatically by a software system.
Examples of use: Criminal identification, control for high security installations, credit card usage verification, and employee identification.
The program to be developed should extract the minutiae (ridge endings, bifurcation points, etc.) from a fingerprint bitmap file and create a compact, unique dataset. This dataset is then stored in a database or compared with one of the database. The goal is to recognize if the person whom the fingerprint belongs to is present in the database.
The product should run under WindowsXP. The fingerprints are present in the form of Windows-Bitmap-Files, which are created with a scanner from printed pictures. The software should have the following properties:
¢ Reading a bitmap file and show it on the screen.
¢ Building a dataset by extracting the minutiae.
¢ Showing the original bitmap with the marked minutiae on the screen.
¢ Storing the dataset in the database.
¢ Comparing a dataset with those in the database.
¢ Giving out the search result.
If there is a match, showing the stored fingerprint on the screen for optical comparison.

Submitted By:
Mygo Informatics Pvt.Ltd, IInd floor, Bhagyarekha Buildings,Vengal Rao Nagar, SR Nagar, Hyd-38


Operating System : Windows XP
Languages : Java 1.6
Tools : Net Beans /MyEclipse


Intel Pentium : 1.0 GHz or above.
RAM (SD/DDR) : 256 MB
Hard Disc : 30 GB

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.doc   Java Finger Print.doc (Size: 62.5 KB / Downloads: 153)
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.pptx   Fingerprint authentication system.pptx (Size: 527.9 KB / Downloads: 141)


Presented By:
ROLL NO.- 84/A


Biometrics is derived from Greek word bios means life and metron means measure. By this method we can uniquely recognize human based on their physical and behavioral traits.


Fingerprints are one of many forms of biometrics used to identify an individual and verify their identity.

It refers to the automated method of verifying a match between two human fingerprints.


The simple fact is that passwords donâ„¢t work very well.

No need to remember passwords or to carry smart cards.

Simple, trusted and convenient user interface to well thought security system.


The figure (2.1) below shows a simple block diagram of a biometric system. The main operations a system can perform are enrollment and test. During the enrollment biometric information of an individual are stored in database, and during the test information are detected and compared with the stored information.


Feature extractor
Template generator
Stored templates
Application device

The first block (sensor) is the interface between the real world (user) and our system (biometric system), it has to acquire all the necessary data. Most of the time it is an image acquisition system, but it can change according to the characteristics we want to consider.


It performs all the necessary pre-processes; it has to remove artifacts from the sensor, to enhance the input (e.g. remove some kind of noise.), to use some kind of normalization, etc.

In this block all the necessary features are extracted. We have to choose which features it extract and how. Moreover we have to do it with certain efficiency (it canâ„¢t take hours). After that, we can have a vector of numbers or an image with particular properties: all those data are used to create the template.

All the characteristics of the source, from extractor is being used by this block to create template.


If the system is performing enrollment then it is stored in this block.


If test is on then the obtained template is simply pass through matcher, it compares it with existing templates. And the decision is sent to output.


Decision taken matcher is sent to application device for further process.

ARCH pattern: In arch pattern ridges enter from one side of the finger and exits other side.

LOOP pattern: In this pattern ridges enter from one side , from a curve and tend to exit same side.

WHORL pattern: In this pattern ridges form a circular pattern in the center of the finger.


Minutia based: The major minutia based features of fingerprint ridges are ridge ending, bifurcation and short point ridge.

Ridge based: Size and shape of fingerprint, number, type, and position singularities (core and deltas), spatial relationship and geometrical attributes of the ridge lines, shape features, global and local texture information, sweat pores, fractal features.

There are three types of sensors used:

Optical sensor: Optical fingerprint imaging involves a digital image capturing of the print using visible light.

Ultrasonic sensor: Ultrasonic sensor use the principles of ultrasound to create the visual image of fingerprint.

Capacitive sensors: Capacitance sensor utilize the principle of parallel plate capacitor to create the fingerprint image.


Pattern Based: Pattern based algorithms compare the basic of fingerprint (arch, loop, whorl) b/w a previously stored template and candidate fingerprint. This requires images be aligned in same orientation.

Minutia based algorithms: It compares several minutia points(ridge ending, bifurcation, and short ridge) extracted from originally stored template with those of candidateâ„¢s fingerprint.

Fingerprint SWAD

Fingerprints donâ„¢t change over time
Widely believed fingerprints are unique
Surgery to alter or remove prints
Finger Decapitation
Corruption of the database
Measure physical properties of a live finger (pulse)


Fingerprint authentication comes under biometrics and is a way fingerprints are compared and matched. Fingerprint authentication has many uses as in security purposes.
The process is simple, as in put your finger down. It does not take a security professional to realize that 10 digit password on sticky notes attached to our monitor is poor security require doing the obvious, and are often done by insiders.
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.doc   BIOMETRICS Finger Print Authentication.doc (Size: 538.5 KB / Downloads: 68)
This paper investigates correlation-based fingerprint authentication schemes that can be used for mobile devices. The investigated algorithms were implemented with a J2ME
Environment on the application layer In order to reduce the resources demanded for the mobile device environment, we also propose a new hierarchical correlation-based scheme based on the idea that the overall authentication can be decomposed into partial autocorrelations. The algorithms have been tested on a J2ME CDC 1.0 emulator of a smart mobile phone.
1. Introduction
Today, a mobile phone can be integrated with a camera, a GPRS, a radio, a MP3 player, a web browser and even a TV. It is foreseeable that future mobile devices will just be
More powerful and function like hand held computers. With this trend of convergence, potential security problems have become more threatening and harmful. This
Urges stronger protections against data leaking and illegitimate use of the device. Biometric authentication can ensure genuine user presence, thus enhancing the privacy protection.
Only recently, a few products of biometric-enabled mobile devices have been announced available to consumers. However, different manufacturers tend to have their own standards and proprietary technology. In most current commercial solutions, the biometric function is embedded in the system hardware and is expensive.
We consider to deploy biometric authentication in the application layer so that better extendability and portability can be achieved for general mobile devices.
Our application is developed using Java 2 Micro Edition (J2ME) [2]. J2ME is a green version of Java. It inherits Java's main benefit of being platform independent as well as
object oriented. Moreover, J2ME was especially designed to fit resource-constrained embedded systems. Its applications can be emulated on a PC during the development stage and then easily uploaded to PDAs or mobile phones, with out the need of expensive system-specific kits and hardware.
J2ME applications should be designed to consume as little
resource as possible. To meet this special requirement, we develop a new hierarchical correlation algorithm for fingerprint authentication on mobile devices.The proposed image correlation. To investigate the authentication performance, a worst case scenario for the correlation-based algorithms was considered where fingerprints with plastic distortions are used for testing in our experiments.
Hierarchical Fingerprint Authentication:
Most existing algorithms for fingerprint matching are based on ridge endings and bifurcations (minutiae) [5]. In those schemes, authentication is approved only if the number of matched minutiae exceeds a predefined threshold. For mobile devices, the fingerprint sensor is usually quite small. Hence, partial and non-overlapping fingerprints are often obtained. This tends to reduce the performance of a minutiae-based fingerprint matching approach. Moreover, minutiae-based algorithms often require a few intermediate image processing steps such as orientation extraction [7, 8] and ridge thinning [1], which will increase the complexity of the J2ME application on mobile devices.
The correlation-based fingerprint matching uses overall inform ation-piedi fingerprint image. A synthetic information provided in a fingerprint image. A synthetic filter is often built as a template using a number of training examples [3]. When a test fingerprint perfectly matches with the filter (template), a well-defined peak will appear in the resulting correlation plane. Otherwise, a flat correlation
output is expected to be observed.
Minimum average correlation energy (MACE) filter The MACE filter was designed to suppress the sidelobes of correlation plane such that a sharp correlation peak
can be produced. Assuming N training images of a subject, each image has a total of d pixels. For the i'th training image, the columns of its 2D Fourier transform is concatenated to form a column vector xi containing d elements. A matrix X from N training images is then defined as
X = [X1,X2 * XN]T.
The 2D MACE filter obtained in the frequency domain is also ordered in a column vector h. The i'th correlation output at the origin is constrained to a prespecified value ui, which can be represented as
where the superscript '+' denotes a conjugate transpose. Note that c(O) is also referred to the correlation peak value. On the other hand, based on Parseval's theorem, the aver-
age of the correlation plane energies, Eave, can be obtained directly from the frequency domain by
where the superscript '*' denotes complex conjugation and D is a diagonal matrix of size d x d whose diagonal elements are the power spectrum of xi Minimizing the average correlation energy Eave subjecting to the constraints placed in (1) leads to the MACE filter solution
Hierarchical correlation-based authentication :-
Conventional correlation-based authentications use fullsized fingerprint images. It has been reported that down-
smln Odimaeto26x56pesrsusinbtr sampling 5OOdpi images to 256 x 256 pixels results in better performance compared to other resolutions [6]. However for mobile devices, this still consumes too much memory and computing power. Therefore, we consider to use partial images at each time of correlation computation. Let us first consider a simple ID case. In the space domain, correlation of r [k] with a target t [k] leads to the following correlation output
The above evaluation can be easily extended for 2D cases. It clearly shows that for autocorrelation, the output peak at the origin is equal to the sum of peak values obtained from the corresponding fractions of the original segment. If the fractions are from other sources, the difference between the peak sum and the original peak value from the target source will not be zero. Based on this idea, we propose a correlation-based hierarchical fingerprint authentication scheme as shown in Figure 2.
The key modules in Figure 2 are described as follows. In the enrollment stage, a template is constructed (possibly offline) from a set of training images based on the MACE
filter design as described previously. The template is represented in the space domain and will be stored in the mobile device.
In the authentication stage, three donut rings will be first extracted from the test fingerprint's core center by defining three concentric circles. For example as shown in Figure 3, the inner donut ring R1 is defined by concentric circles."Ciand er donut ring R, is defined by C1 and C2. The outer donut ring R2 iS defined by C2 and C3. The overall donut ring R3 is defined by Ci and C3. Corresponding parts in the template will also be extracted using concentric circles with the same diameters, namely T1, T2 and T3. The donut rings R1, R2 and R3 from the test fingerprint are then correlated with their corresponding template parts T1, T2 and T3 respectively, yielding three correlation peak values Pi, P2 and p3.
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.doc   FINGERPRINT AUTHENTICATION.doc (Size: 377 KB / Downloads: 60)
Biometrics, the science of applying unique physical or behavioral characteristics to verify
an individual’s identity, is the basis for a variety of rapidly expanding applications for
both data security and access control. Numerous biometrics approaches currently exist,
including voice recognition, retina scanning, facial recognition and others, but fingerprint
recognition is increasingly being acknowledged as the most practical technology for low
cost, convenient, and reliable security. Fidelica Microsystems’ new and exclusive
technology overcomes the limitations of previous systems and sets a new standard for
compact, reliable and low-cost fingerprint authentication.
Although fingerprints have been used as a means of identification since the middle of the
19th century, modern fingerprint authentication technology has little in common with the
ink-and-roll procedure that most people associate with fingerprinting. In order to
appreciate the distinction and understand modern fingerprint authentication technology,
one needs to understand the basis of a fingerprint.
A fingerprint is composed of ridges, the elevated lines of flesh that make up the various
patterns of the print, separated by valleys. Ridges form a variety of patterns that include
loops, whorls and arches as illustrated in Fig. 1. Minutiae are discontinuities in ridges,
and can take the form of ridge endings, bifurcations (forks), crossovers (intersections)
and many others.
Fingerprint authentication is based on a subset of features selected from the overall
fingerprint. Data from the overall fingerprint is reduced (using an algorithm application
that is usually unique to each vendor) to extract a dataset based on spatial relationships.
For example, the data might be processed to select a certain type of minutiae or a
particular series of ridges. The result is a data file that only contains the subset of data
points . the full fingerprint is not stored, and cannot be reproduced from the data file.
This is in contrast with ink-and-roll fingerprinting (or its modern optical equivalent),
which is based on the entire fingerprint.
Modern forensic fingerprinting, with files on the order of 250kB per finger, is used in large scale, one-to-many searches with huge databases, and can require hours for
verification. Fingerprint authentication, using files of less than 1000 bytes, is used for one-to-one verification and give results in a few seconds.
In use, fingerprint authentication is very simple. First, a user enrolls in the system by
providing a fingerprint sample. The sensor captures the fingerprint image. The sensor
image is interpreted and the representative features extracted to a data file by algorithms
either on a host computer or a local processor (in applications such as cellular handsets).
This data file then serves as the users individual identification template. During the
verification process, the sequence is repeated, generating an extracted feature data file. A
pattern matching algorithm application compares the extracted feature data file to the
identification template for that user, and the match is either verified or denied. State-ofthe-art processor, algorithm and sensor systems can perform these steps in a second or
Fingerprint authentication can be based on optical, capacitance or ultrasound sensors.
Optical technology is the oldest and most widely used, and is a demonstrated and proven
technology, but has some important limitations. Optical sensors are bulky and costly, and
can be subject to error due to contamination and environmental effects. Capacitance
sensors, which employ silicon technology, were introduced in the late 1990’s. These
offer some important advantages compared to optical sensors and are being increasingly
applied. Ultrasound, utilizing acoustic waves, is still in its infancy and has not yet been
widely used for authentication.
Silicon-based sensors have a two-dimensional array of cells, as shown in Fig. 2. The size
and spacing of the cell is designed such that each cell is a small fraction of the ridge
spacing. Cell size and spacing are generally 50 microns, yielding a resolution of up to
500 dpi, the FBI’s image standard. When a finger is placed on the sensor, activating the
transistors that underlay each individual cell captures the image. Each cell individually
records a measurement from the point on the finger directly above the cell as shown
Though different vendors use different physical properties to make the measurement, the
data is recorded as the distance, or spacing, between the sensor surface and that part of
the finger directly above it. However, distance measurement has some inherent
weaknesses, which are overcome by Fidelica Microsystems’ novel technology, as
described below.
The set of data from all cells in the sensor is integrated to form a raw, gray-scale fingerprint image as shown in Fig. 4. Fingerprint imaging using a continuum of distance
measurements results in an 8-bit gray scale image, with each bit corresponding to a
specific cell in the two-dimensional array of sensors. The extreme black and white
sections of the image correspond to low and high points on the fingerprint. Only the high
points on the fingerprint are of interest, since they correspond to the ridges on the
fingerprint that are used to uniquely identify individuals. Therefore, the 8-bit gray-scale
image must be converted into a binary, or bitonal, image using an additional procedure in the feature extraction algorithm. This process is a common source of error, since there
could be many false high points or low points due to dirt, grease, etc., each of which
could result in a false minutia extraction, and hence, introduce additional error in the
matching process.
The feature extraction algorithm is then used extract the specific features from the
fingerprint that make up the individual’s unique data file. This data file serves as the
user’s individual identification template, which is stored on the appropriate device.
During verification, the imaging and feature extraction process is repeated, and the
resulting data file compared with the users identification template by pattern matching
software to verify or deny the match.

Fidelica Microsystems’ sensor technology is unique among commercially available
fingerprint authentication systems. Fidelica Microsystems uses a thin film-based sensor
array that measures pressure to differentiate ridges from valleys on a fingerprint. This is
in contrast to distance measurement, which is the basis of all other commercially
available sensors, whether optical or capacitance (silicon-based).
The sensor is architecturally and physically similar to the silicon-based sensors in terms of cell size and spacing, and therefore offers similar resolution. However, when a finger in placed over the sensor, only the ridges come in contact with the individual pressure sensing cells in the two-dimensional array, whereas no other part of the finger contacts the sensor. As a result, only those cells that experience the pressure from the ridges undergo a property change. To record the image, the array is scanned using proprietary electronic circuits. With an appropriate threshold setting, a distinction can be made between those cells that experience pressure and those that do not.
The Fidelica Microsystems sensor employs a resistive network at each cell location.
Each cell incorporates a structure similar to those employed in the micro-electromechanical system (MEMS) industry. Upon the application of a fingerprint, the
structures under the ridges of the fingerprint experience a deflection, and a change in
resistance results. This change in resistance is an indication of the presence of a ridge
above the cell being addressed. In principle, although the resistance value is an analog
value, the difference between the resistance in the pressed and unpressed states is large
enough that, with an appropriate threshold setting, one can easily distinguish between the
presence or absence of a ridge with high resolution and accuracy.
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10-08-2011, 11:19 PM

I need an algorithm which compare finger print images upto 70%
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.pdf   FINGERPRINTS.pdf (Size: 14.48 KB / Downloads: 13)


Fingerprints are the oldest and most widely used form of biometric identification. Despite
the widespread use of fingerprints, there is little statistical theory on the Uniqueness of
fingerprint minutiae. A critical step in studying the statistics of fingerprint minutiae is to
reliably extract minutiae from the fingerprint images. However, fingerprint images are
rarely of perfect quality. They may be degraded and corrupted due to variations in skin
and impression conditions. Thus, image enhancement techniques are employed prior to
minutiae extraction to obtain a more reliable estimation of minutiae locations.
In this dissertation, I firstly provide discussion on the methodology and
implementation of techniques for fingerprint image enhancement and minutiae extraction.
Experiments using a mixture of both synthetic test images and real
fingerprint images are then conducted to evaluate the performance of the implemented
techniques. In combination with these techniques, preliminary results on the statistics of
fingerprint images are then presented and discussed.

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