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Fingerprint Recognition future directions full report
Post: #1


Fingerprint Recognition :Future Directions

Presented BY:
Salil Prabhakar
Digital Persona Inc.

Fingerprint Applications


Computer Network Logon,Electronic Data Security,E-Commerce,InternetAccess,ATM, Credit Card,Physical Access Control,Cellular PhonesPersonal Digital Assistant,Medical Records,Distance Leaning, etc.


National ID card,Correctional Facilities,Driverâ„¢s License,Social Security,Welfare Disbursement,Border Control,Passport Control, etc


Corpse IdentificationCriminal Investigation,Terrorist Identification,Parenthood determination,Missing Children, etc.

Fingerprint Application Functionality

Positive Identification
Is this person truly know to the system
Commercial applications (network logon)
Desirable: low cost and user-friendly

Large Scale Identification
Is this person in the database
Government and Forensic applications (prevent double dipping; multiple passports)
Desirable: high throughput with little human intervention

Surveillance and Screening
Is this a wanted person
Airport watch list
Fingerprints are not suitable

Reasons for Accuracy Challenges
Information Limitation Due to individuality, poor presentation and inconsistent acquisition.

Design and choice of representation (features) and quality of feature extraction algorithms (especially for poor quality fingerprints)

Invariance Limitation
Incorrect modeling of invariant relationships among features

Fingerprint Individuality Estimation

Accuracy; Information Limitation
Assumptions for theoretical individuality estimation consider only minutiae (ending and bifurcation) features minutiae locations and directions are independent minutiae locations are uniformly distributed correspondence of a minutiae pair is an independent event quality is not explicitly taken into account ridge frequency is assumes to be constant across population and spatially uniform in the same finger analysis of matching of different impressions of the same finger binds the parameters of the probability of matching prints from different fingers an alignment between two fingerprints has been established.
Probability of a False Correspondence
Accuracy; Information Limitation; Fingerprint Individuality Estimation
m = no. of minutiae in template
n = no. of minutiae in input
= no. of corresponding minutiae based on location (x,y) alone
q = no. of corresponding minutiae based on location and direction ()
A = area of overlap between input and template
C = area of tolerance region = r02/A

Probability that one of one input minutiae matches any of the m template minutiae:
Probability that two of two input minutiae matches any of the m template minutiae:
Information Limitation: Conclusion
Accuracy; Information Limitation
There is an incredible amount of information content in fingerprints
A minutiae-based fingerprint identification system can distinguish between identical twins
The performance of state-of-the-art automatic fingerprint matchers do not even come close to the theoretical performance
Performance of fingerprint matcher is depended on the fingerprint class and thus may depend upon target population
Fingerprint classification may not be very effective in genetically related population
Fingerprint identification accuracy may suffer in certain demographics

Conventional Representations

Sequential design based on the following modules: Segmentation, local ridge orientation estimation (singularity and more detection), local ridge frequency estimation, fingerprint enhancement, minutiae detection, and minutiae filtering and post-processing.

Ridge Feature-based

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

Representations: Future Directions

Improvement of current representations through robust and reliable domain-specific image processing techniques such as:
Model-based orientation field estimation
Robust image enhancement and masking New richer representations Fusion of various representations

Matching: Future Directions
Accuracy; Invariance Limitation

Alignment remains a difficult problem “ develop alignment techniques that remain robust under the presence of false features
Understand and model fingerprint deformation
Fusion of various matchers (based on the same or different representations)
Multiple Biometrics; Fusion
A decision (and lower) level fusion of multiple biometrics can improve performance
In identification systems, fusion can also improve speed
Independence among modalities is key
Even combination of correlated modalities can be no worse than the best performing modality alone
Best combination scheme would be application dependent

Performance Evaluation

Evaluation types: technology, scenario, operational
Dependent on composition of the population (occupation, age,demographics, race), the environment, the system operational mode, etc
Ideally, characterize the application-independent performance in laboratory and predict technology, scenario, and operational performances
Standardization and independent testing
Parametric and non-parametric estimation of confidence intervals and database size
Parametric and non-parametric and statistical modeling of inter-class and intra-class variations;

Usability, Security, Privacy

Biometrics are not secrets and not revocable
Encryption, secure system design, and liveness detection solve this problem
Unintended functional scope; unintended application scope; covert acquisition
Legislation; self-regulation; independent regulatory organizations
Biometric Cryptosystems: fingerprint fuzzy vault
Similarity metric in encrypted domain
Variable and unordered representation
Performance loss; ROC remains the bottleneck

Post: #2

Biometrics is derived from Greek word bios means life and metron means measure.
It is the study of methods for uniquely recognizing human based upon one or more physical or behavioral traits.
It refers to the automated method of verifying a match between two human fingerprints.
Fingerprints are one of many forms of biometrics used to identify an individual and verify their identity. This article touches two major classes of algorithms (minutia and pattern) and four sensor designs (optical, ultrasonic, passive capacitance and active capacitance).
Will use it, convenience. The simple fact is that passwords donâ„¢t work very well. It has been more than 16 years since the introduction of commercial fingerprint authentication systems. Yet they are just now gaining broad acceptance. We should not be surprised. Many technologies required several years before the right combination of factors allowed them to become ubiquitous. If one looks back to laptop computers, cell phones, fax machines, pagers, laser printers and countless other devices, one will realize most had long gestation periods. Biometrics is now at the acceptance crossroads. What will propel them to common usage
Convenience first “ There is the reason end-users should use fingerprint authentication in the IT world, i.e. security, and there is the reason they establish, but very expensive to maintain. Just ask the any help desk manager in a major corporation. More than 50 percent of all help desk cells are related to password 3 & MAC218: for lost, forgotten or otherwise useless. Count all passwords you use everyday and often have to change once a month. Password management is a nightmare for MIS managers & users. Fingerprint authentication eliminates the problem and headache.
Other authentication mechanisms such as tokens, smart cards, etc. require you to carry something. This is better than a password, but easies to lose. Think about losing your credit card or driving license. Losing your corporate network access is a lot worse. Information is valuable and harder to track than money.
Fingerprints can also acts as a simple, trusted and convenient user-interface to a well thought security architecture. The two components need each other to provide truly effective security. A user authenticated via fingerprints can take advantage of a solid security system minimal education.
Simple truth is that users donâ„¢t trust what they donâ„¢t understand. Most IT security concepts are incomprehensible to the common user. Explaining public and private keys, key recovery systems and digital certificates is beyond the skills of even skilled MIS professionals. Most users have no concept of encryption algorithms and their implementations, nor do they want to understand. Users want simple, trusted, security.
Simple, as in put your finger down. It does not take a security professional to realize that passwords on sticky notes attached to your monitor are poor security. Most breaches of security require doing the obvious, and are often done by insiders.
Trusted, as in had stood the test of time. Fingerprints have been used for identification for over 100 years. They are the standard without question. In addition to signatures, fingerprints are the only other form of identification that has a legal standing. A key issue of trust is privacy. The best way to maintain that is to store a template of unique fingerprint characteristics instead of entire print. This is sufficient for one to one or too many matching and eliminates the need for a database of searchable fingerprints.
The diagram (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, during the test, and during the test information are detected and compared with the stored information.
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. Sensor (red in color) is shown in picture (2.2) below.


The second block performs all the necessary pre-processing; 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 third block we have to extract the features we need. This step is really important; 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.
A template is a synthesis of all the characteristics we could extract from the source; it has to be as short as possible (to improve efficiency) but we canâ„¢t discard to many details, thus losing discrimination.
Then if it is performing enrollment, then the template is simply stored somewhere (it can be stored in on a card or within a database). If it is performing the matching phase, the obtained template is passed to matcher that compares it with other existing templates, estimating the distance between them using any algorithm (e.g. Hamming distance). The decision matcher has taken is sent as output, so that it can be used for any purpose (e.g. it can allow the entrance in restricted areas).
The analysis of fingerprints for matching purposes generally requires the comparison of several features of the print pattern. These include print patterns, which are aggregate characteristics of ridges, and minutia points, which are unique features found within patterns.
It is also necessary to know the structure and properties of human skin in order to successfully employ some of the imaging technologies.
3.1 Patterns:
There are three basic patterns of fingerprint ridges are the Arch, Loop, and Whorl.
3.1.1 Arch Pattern:
An arch is a pattern where the ridges enter from one side of a finger, rise in the centre forming an arc, and them exit the other side of the finger. Refer fig. 3.1
3.1.2 Loop Pattern:
The loop pattern is a pattern where the ridges enter from one side of a finger, from a curve, and tend to exit from the same side they enter. Refer fig 3.1
3.1.3 Whorl Pattern:
In the whorl pattern, ridges form a circular pattern around a central point on the finger. Scientists have found that of family members often share the same general patterns, leading to the belief that these patterns are inherited. Refer fig 3.1

3.2 Features:
3.2.1 Minutia based feature:
The major minutia features of fingerprint ridges are: ridge ending, bifurcation and short point ridge (or dot). The ridge ending is the point at which a ridge terminates. Bifurcations are points at which a single ridge splits into two ridges. Short ridges (or dots) are the ridges which are significantly shorter than the average ridge length on the fingerprint.
Sequential design based on the following modules: Segmentation, local ridge orientation estimation (singularity and more detection), local ridge frequency estimation, fingerprint enhancement, minutiae detection, and minutiae filtering and post-processing. Refer figure 3.2
3.2.2 Ridge based feature:
Size and shape of fingerprint, number, type, and position of singularities (cores and deltas), spatial relationship and geometrical attributes of the ridge lines, shape features, global and local texture information, sweat pores, fractal features.

Minutiae and patterns are very important in the analysis of fingerprints since no two fingers have shown to be identical.
A fingerprint sensor is an electronic device used to capture a digital image of the fingerprint pattern. The captured image is called a live scan is digitally processed to create a biometric template (a collection of extracted features) which is stored and used for matching. This is an over view of some of the more commonly used fingerprint sensor technologies.
4.1 Optical Sensors:
Optical fingerprint imaging involves capturing a digital image of the print using visible light. This type of sensor is, in essence a specialized digital camera.
The top layer of the sensor, where the finger is placed, is known as the touch surface. Beneath this layer is a light emitting phosphor layer which illuminates the surface of the finger. The light reflected from the finger passes trough the phosphor layer to an array of the solid state pixels (a charge coupled device) which captures a visual image of the fingerprint. A scratched or dirty touch surface can cause a bad image of the fingerprint. A disadvantage of this type of the sensor is the fact that the imaging capabilities are affected by the quality of skin on the finger. For instance, a dirty or or marked finger is difficult to image properly.
Also, it is possible for an individual to erode the outer layer of skin on the fingertips to the point where the fingerprint is no longer visible. It can also be easily fooled by an image of a fingerprint if not coupled with a live finger detector. However, unlike capacitive sensors, this sensor technology is not susceptible to electrostatic discharge damage. Refer fig below,

4.2 Ultrasonic Sensors:
Ultrasonic sensors make use of the principles of medical ultrasonography in order to create visual images of the fingerprint. Unlike optical imaging, ultrasonic sensors use very high frequency sound waves to penetrate the epidermal layer of skin.
The sound waves are generated using piezoelectric transducers and reflected energy is also measured using piezoelectric materials. Since the dermal skin layer exhibits the same characteristics pattern of the fingerprint, the reflected wave measurements can be used to form an image of the fingerprint this eliminates the need for clean, undamaged epidermal skin and a clean sensing surface.
4.3 Capacitance Sensors:
Capacitance sensors utilize the principles associated with the capacitance in order to form fingerprint images. In this method of imaging, the sensor array pixels each ac as one parallel plate capacitor, the dermal layer (which is electronically conductive) acts as other plate, and the non conductive epidermal layer acts as a dielectric.

Matching algorithms are used to compare previously stored templates of fingerprints against candidate fingerprints for authentication purposes. In order to do this either the original image must be directly compared with the candidate image or certain features must be compared.
5.1 Pattern Based:
Pattern based algorithms compare the basic fingerprint (arch, whorl, and loop) between a previously stored template and a candidate fingerprint. This requires that the images be aligned in the same orientation. To do this, the algorithms find a central point in the fingerprint image and centers on that. In a pattern based algorithm the template contains the type, size, and orientation of patterns within the aligned fingerprint image. The candidate image is graphically compared with the template to determine the degree to which they match.
5.2 Minutia Based Algorithms:
Minutia based algorithms compare several minutia points (ridge ending, bifurcation, and short ridge) extracted from the original image stored in a template with those extracted from a candidate fingerprint. Similar to the pattern based algorithm, the minutia based algorithm must be aligned a fingerprint image before extracting feature points. This alignment must be performed so that there is a frame of reference. For each minutia point, a vector is stored into the template in the form:
Mi = (type, xi, yi, @i, w)
mi is the minutia vector
type is the feature (ridge ending, bifurcation, short ridge)
xi is the x co-ordinate of the location
yi is the y co-ordinate of the location
@i is the angle of orientation of the minutia
W is the weight based n the quality of the image at that location.
It is important to note that an actual image of the print is not stored as a template under this scheme. Before the matching process begins, the candidate image must be aligned with the template co-ordinates and rotation. Features from the candidate image are then extracted and compared with the information in the template. Depending on the size of the input image, there can be 10-100 minutia points in a template. A successful match typically only requires 7-20 points to match between the two fingerprints.
1. Fingerprints donâ„¢t change over time.
2. Widely believed fingerprints are unique.
3. Do not need to remember passwords.
4. Do not need carry smartcards, etc.
1. Scars on fingers can create problem in matching of fingerprint.
2. Finger Decapitation.
3. Corruption of the database.
7.1 Commercial:
1. Computer Network Logon,
2. Electronic Data Security,
3. E-Commerce,
4. Internet Access,
5. ATM card, Credit Card,
6. Physical Access Control,
7. Cellular Phones
8. Personal Digital Assistant,
9. Medical Records,
10. Distance Leaning, etc.
7.2 Government:
1. National ID card,
2. Correctional Facilities,
3. Driverâ„¢s License,
4. Social Security,
5. Border Control,
6. Passport Control, etc
7.3 Forensic:
1. Corpse Identification
2. Criminal Investigation,
3. Terrorist Identification, etc.
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.
Trusted, as in had stood the test of time. Fingerprints have been used for identification for over 100 years. They are the standard without question. In addition to signatures, fingerprints are the only other form of identification that have legal standing in our country.
The best way to maintain that is to store a template of unique fingerprint characteristics instead of entire print. This is sufficient for one to one or one to many matching and eliminates the need for a database of searchable fingerprints.
4. SENSORS 9-10

Post: #3


Bachelor of Technology
Electrical Engineering
Under the Guidance of
Prof. P. K. Sahu

Department of Electrical Engineering
National Institute of Technology

Human fingerprints are rich in details called minutiae, which can be used as
identification marks for fingerprint verification. The goal of this project is to develop a complete
system for fingerprint verification through extracting and matching minutiae. To achieve good
minutiae extraction in fingerprints with varying quality, preprocessing in form of image
enhancement and binarization is first applied on fingerprints before they are evaluated. Many
methods have been combined to build a minutia extractor and a minutia matcher. Minutia
marking with special consideration of the triple branch counting and false minutiae removal
methods are used in the work. An alignment-based elastic matching algorithm has been
developed for minutia matching. This algorithm is capable of finding the correspondences
between input minutia pattern and the stored template minutia pattern without resorting to
exhaustive search. Performance of the developed system is then evaluated on a database with
fingerprints from different people.
Post: #4


What is A Fingerprint?

A fingerprint is the feature pattern of one finger (Figure 1.1.1). It is believed with strong
evidences that each fingerprint is unique. Each person has his own fingerprints with the
permanent uniqueness. So fingerprints have being used for identification and forensic
investigation for a long time.
Figure1.1.1 A fingerprint image acquired by an Optical Sensor
A fingerprint is composed of many ridges and furrows. These ridges and furrows
present good similarities in each small local window, like parallelism and average width.
However, shown by intensive research on fingerprint recognition, fingerprints are not
distinguished by their ridges and furrows, but by Minutia, which are some abnormal
points on the ridges (Figure 1.1.2). Among the variety of minutia types reported in
literatures, two are mostly significant and in heavy usage: one is called termination,
which is the immediate ending of a ridge; the other is called bifurcation, which is the
point on the ridge from which two branches derive.
Post: #5


Fingerprint matching is the process used to determine whether two sets of fingerprint ridge detail come from the same finger.

Extensive research has been done on fingerprints in humans.
Two of the fundamentally important conclusions that have risen from research are:
(1) a person's fingerprint will not naturally change structure after about one year after birth and
(2) the fingerprints of individuals are unique.
Even the fingerprints in twins are not the same. In practice two
humans with the same fingerprint have never been found

A fingerprint is comprised of ridges and valleys.
The ridges are the dark area of the fingerprint and the valleys are the white area that exists between the ridges.
These points are also known as the minutiae of the fingerprint. The most commonly used minutiae in current fingerprint recognition technologies are ridge endings and bifurcations because they can be easily detected by only looking at points that surround them.
Post: #6
pls upload matlab code for graphical user interface also for Fingerprint Recognition
Post: #7
more details on Fingerprint Recognition , please download the attached files from previous page.
Post: #8
Where can i get the source code ?
Post: #9
Presented by:
Soumyadeep Chakraborty
Payel Ghosh
Prosenjit Dey

For the successful completion of our presentation we would like to thank the Computer Science & Engineering department faculty for all their support and coordination.
We would also like to thank the following sources for providing valuable information for our topic :
Post: #10
Full matlab manual for Fingerprint Recognition future directions
Post: #11
To get more information about the topic " Fingerprint Recognition future directions full report" please refer the link below
Post: #12
to get information about the topic "fingerprint recognition" full report ppt and related topic refer the link bellow

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