Introduction to Biometrics
Dr. Bhavani Thuraisingham
The University of Texas at Dallas
Lecture #7
Biometric Technologies: Finger Scan
September 14, 2005
Outline
 Introduction
 Basic Terms
 Technologies
 Finger Scan Process
 Feature Extraction
 Classification
 Accuracy and Integrity
 Biometric Middleware
 Strengths and Weaknesses
 Biometric vs. Non Biometric Fingerprinting
 Research Directions
 Project Related Information
References
 Course Text Book, Chapter 4
 http://www.biometricsinfo.org/fingerprintrecognition.htm
Introduction
 What is Finger-Print Scanning
- Fingerprint scanning is the acquisition and recognition of
a person’s fingerprint characteristics for identification
purposes.
- This allows the recognition of a person through
quantifiable physiological characteristics that verify the
identity of an individual.
 Methods
- There are basically two different types of finger-scanning
technology that make this possible.
One is an optical method, which starts with a visual image
of a finger.
- The other uses a semiconductor-generated electric field
to image a finger.
-
Introduction (Concluded)
 There are a range of ways to identify fingerprints.
- traditional police methods of matching minutiae
- straight pattern matching
- Ultrasonics
 Fingerprint revenues are projected to grow from $144.2m in
2002 to $1,229.8m in 2007. Fingerprint revenues are expected
to comprise approximately 30% of the entire biometric
technologies
 Applications
- to access networks and PCs, enter restricted areas, and to
authorize transactions.
 Deployed in many locations (discussed in text book)
Basic Terms
 Components
- Image acquisition systems, image processing
components, template generation and matching
components, storage components
 Surface on which finger is placed is Platen or Scanner
 Finger scan module
- consists of platen + printed circuit board + standard
connector that transmits digitized information to a
peripheral or standalone device
Example Technologies
 Optical Technology
- Oldest technology
- Camera registers the image of the fingerprint against a
coated glass or plastic platen
- Black, gray and white lines
 Silicon Technology
- Silicon chip embedded in a platen
- High image quality
- Commercially available since around 1998
 Ultrasound Technology
Transmit acoustic waves to the finger and generating
images
-
Process
 Image Acquisition
- Measured in terms of dots per inch
- Center of the finger print must be placed on the platen
- Need appropriate size for platen
 Image Processing
- Eliminate gray areas from image
- Convent gray pixels to black and white pixels
 Location of Distinctive Characteristics
- Fingerprints consists of ridges and valleys
- Swirls, loops, arches, deltas
- Ridges and valleys are characterized by irregularities
-
called minutiae
A finger scan image can produce about 15-50 minutiae
Process (Concluded)
 Template Creation
- Vendors use proprietary algorithms
- Depends on the following
 Location
and angle of a minutiae point
 Distance and position of minutiae relative to the core
 Type and quality of the minutiae
- Need to eliminate sweat, scars, dirt, etc.
 Template matching
- May depend on the number of minutiae matched
Methods of Finger Printing
Minutiae vs. Pattern matching
 Minutiae
- Most of the finger-scan technologies are based on
minutiae
 Pattern Matching
- Feature extraction and template generation based on
series of ridges as opposed to discrete points
- Advantage: Minutiae points affected by wear and tear
- Disadvantage: Sensitive to proper placement of finger;
large storage for templates
 Correlation
Michigan State University of developing correlation based
methods
-
Feature Extraction
 The human fingerprint is comprised of various types of ridge
patterns
-
left loop, right loop, arch, whirl, and tented arch.
Loops make up nearly 2/3 of all fingerprints
whirls are nearly 1/3
5-10% are arches.
Figure 1

Source: Book, URL
Feature Extraction (Continued)
 Minutiae (Figure 1), the discontinuities that interrupt the
otherwise smooth flow of ridges, are the basis for most
fingerprint authentication.
 Many types of minutiae exist, including dots (very small
ridges), islands (ridges slightly longer than dots), ponds or
lakes - - -  The core is the inner point, normally in the middle of the print,
around which swirls, loops, or arches center.
 Deltas are the points, normally at the lower left and right hand
of the fingerprint, around which a triangular series of ridges
center.
 The ridges are also marked by pores, which appear at steady
intervals.
Feature Extraction (Continued)
 Once a high-quality image is captured, there are a several
steps required to convert its distinctive features into a
compact template.
- This process, known as feature extraction, is at the core
of fingerprint technology.
fingerprint vendor has a proprietary feature extraction
mechanism
 The image must then be converted to a usable format.
- If the image is grayscale, areas lighter than a particular
threshold are discarded, and those darker are made black
The ridges are then thinned from 5-8 pixels in width down
to one pixel, for precise location of endings and
bifurcations.
-
-
Feature Extraction (Continued)
 Minutiae localization begins with this processed image.
- At this point, even a very precise image will have
-
distortions and false minutiae that need to be filtered out
an algorithm may search the image and eliminate one of
two adjacent minutiae, as minutiae are very rarely
adjacent.
Anomalies caused by scars, sweat, or dirt appear as false
minutiae, and algorithms locate any points or patterns
that do not make sense
A large percentage of would-be minutiae are discarded in
this process.
Feature Extraction (Concluded)
 The point at which a ridge ends, and the point where a
bifurcation begins, are the most rudimentary minutiae, and
are used in most applications.
 There is variance in how exactly to situate a minutia point:
- whether to place it directly on the end of the ridge, one
pixel away from the ending, or one pixel within the ridge
ending
 Once the point has been situated, its location is commonly
indicated by the distance from the core, with the core serving
as the 0,0 on an X,Y-axis.
 Some vendors classify minutia by type and quality. The
advantage of this is that searches can be quicker
Fingerprint Classification
 Large volumes of fingerprints are collected and stored
everyday in a wide range of applications including forensics,
access control, and driver license registration.
 An automatic recognition of people based on fingerprints
requires that the input fingerprint be matched with a large
number of fingerprints in a database (FBI database contains
approximately 70 million fingerprints).
 To reduce the search time and computational complexity, it is
desirable to classify these fingerprints in an accurate and
consistent manner so that the input fingerprint is required to
be matched only with a subset of the fingerprints in the
database.
Fingerprint Classification (Continued)
 Fingerprint classification is a technique to assign a fingerprint
into one of the several pre-specified types already established
in the literature which can provide an indexing mechanism.
 Fingerprint classification can be viewed as a coarse level
matching of the fingerprints.
 An input fingerprint is first matched at a coarse level to one of
the pre-specified types and then, at a finer level, it is
compared to the subset of the database containing that type
of fingerprints only.
Fingerprint Classification (Concluded)
 Michigan State University has developed an algorithm to
classify fingerprints into five classes,
- whirl, right loop, left loop, arch, and tented arch.
- The algorithm separates the number of ridges present in
four directions (0 degree, 45 degree, 90 degree, and 135
degree) by filtering the central part of a fingerprint with a
bank of Gabor filters.
- This information is quantized to generate a FingerCode
which is used for classification.
- Classification is based on a two-stage classifier which
uses a K-nearest neighbor classifier in the first stage and
a set of neural networks in the second stage.
- The classifier is tested on 4,000 images in the NIST-4
database with about 90% accuracy
Image Enhancement
 A critical step in automatic fingerprint matching is to
automatically and reliably extract minutiae from the input
fingerprint images.
 However, the performance of a minutiae extraction algorithm
relies heavily on the quality of the input fingerprint images.
 In order to ensure that the performance of an automatic
fingerprint identification/verification system will be robust
with respect to the quality of the fingerprint images, it is
essential to incorporate a fingerprint enhancement algorithm
in the minutiae extraction module.
 Michigan State University has developed algorithms for
enhancement
Image Enhancement (Concluded)
Source: URL
Accuracy and Integrity
 In most cases, false negatives (a failure to recognize a
legitimate user) are more likely than false positives.
 Overcoming a fingerprint system by presenting it with a "false
or fake" fingerprint will be difficult
 Sensors on the market use a variety of means to circumvent
the problems.
- Problem: Someone may attempt to use latent print residue
on the sensor just after a legitimate user accesses the
system; Presenting a finger to the system that is no
longer connected to its owner.
- Solutions: Sensors attempt to determine whether a finger
is live, and not made of latex; Detectors for temperature,
blood-oxygen level, pulse, blood flow, humidity, or skin
conductivity would be integrated.
Biometric Middleware
 Enables various biometric technologies
 Allows match / no-match decisions made by core
technologies to provide authentication to various applications
 Similar to the concept of middleware in systems
- Integrates applications and resources
 Flexible middleware
- Solutions adapted for applications
Biometric Middleware (Concluded)
Building
Entry
PC Entry
Immigration
Entry
Middleware
Applications
Middleware
Middleware
Services
Finger-scan
Face-scan
Iris-scan
Strengths and Weaknesses
 Strengths
- Proven technology and high level of accuracy
- Many deployments
- Easy to use
- Can enroll multiple fingers
 Weaknesses
- Some users do not have clear fingerprints
- Over time image quality deteriorates
- Privacy concerns
Biometric vs. Non Biometric Fingerprinting
 Fingerprinting, is the acquisition and storage of the image of
the fingerprint.
 Two types of systems
- Forensic (AFIS – Automatic Fingerprint Identification
System)
- Biometric system
 AFIS stores images of fingerprints; need large amount if
storage.
 Biometric systems store particular data about the fingerprint
in a much smaller template.
- After the data is extracted, the fingerprint is not stored
- The full fingerprint cannot be reconstructed from the
fingerprint template.
- Used to log on to PC
Biometric vs. Non Biometric Fingerprinting
(Concluded)
 Response time - AFIS systems may take hours to match a candidate, while
fingerprint systems respond within seconds
 Cost - an AFIS capture device is very expensive. A PC peripheral fingerprint
device is much cheaper
 Accuracy - an AFIS system might return the top 5 candidates with the intent
of locating or questioning the top suspects. Fingerprint systems are
designed to return a single yes/no answer.
 Scale – AFIS systems scalable to thousands and millions of users.
Fingerprint systems are and do not require significant processing power.
 Capture – AFIS systems are designed to use the entire fingerprint.
Fingerprint systems use only the center of the fingerprint.
 Storage – AFIS systems need large storage. Fingerprint systems do not
 Infrastructure – AFIS systems require a backend infrastructure for storage,
matching, and duplicate resolution. Fingerprint systems rely on a PC or a
peripheral device for processing and storage.
Some Research Directions
 New Biometric Technologies
 Less False Positives and False Negatives
 Better Performance
 Secure Biometrics
 Privacy
 Societal Impact
Summary
 Most popular biometric technology
 Fairly high accuracy
 Market expected to grow a great deal
 Feature extraction is the key mechanism
 Minutiae based and non Minutiae based methods for
Biometric matching
 Differences between systems used for forensic applications
and biometric systems
Some Project Related
Information
Dr. Bhavani Thuraisingham
The University of Texas at Dallas
Graduate Student: Pallabi Parveen
September 14, 2005
TA Office Hours
 Nathalie Tsybulnik
 7-10pm Monday in ECSS 3.403
 Tuesday from 10.00-11.00am she will usually be in the general
lab downstairs.
Some Tools for Project
 http://java.sun.com/products/java-
media/jai/forDevelopers/jaifaq.html#what
- Java Advanced Imaging Toolkit (product of Sun
Microsystems)
- Can Download
 http://www.mathworks.com/products/image/
- Matlab Image Processing
- Matlab available in some of our labs
- Cannot download
 CMU Voice Recognition Open Source System Sphinx
- http://cmusphinx.sourceforge.net/html/cmusphinx.php
Face Recognition
 Given at CMU, involves face recognition using neural networks.
 32 images of each of 20 students in the class were taken with a
variety of head positions and facial expressions.
 These images were then used to train and test neural networks to
recognize individual people, and to recognize different face
 Source:
http://www.cs.cmu.edu/afs/cs.cmu.edu/user/avrim/www/ML94
/face_homework.html
-
Finger Print Recognition
 Fingerprint Minutiae from Latent and Matching Tenprint Images
 NIST Special Database 27 contains latent fingerprints from crime
scenes and their matching rolled fingerprint mates.
 Source: http://www.nist.gov/srd/nistsd27.htm
 http://www.itl.nist.gov/iad/894.03/databases/defs/dbases.html#fin
glist\
Fingerprint Research
 NIST 8-Bit Gray Scale Images of Fingerprint Image Groups
(FIGS)
 2000 8-bit gray scale fingerprint image pairs including
classifications
 400 fingerprint pairs from each of the five classifications Arch, Left and Right Loops, Tented Arch, Whirl)
 Source: http://www.nist.gov/srd/nistsd4.htm
Iris Recognition
 CASIA Iris Image Database( ver 1.0) includes 756 iris images from
108 eyes (hence 108 classes).
 For each eye, 7 images are captured in two sessions, where three
samples are collected in the first session and four in the second
session.
 Source: http://www.sinobiometrics.com/casia%20iris.htm
Keystroke Dynamics as a Biometric for
Authentication
 An emerging non-static biometric technique that aims to
identify users based on analyzing habitual rhythm patterns in
the way they type [Fabian Monrose et al.].
 Source: http://www.cs.jhu.edu/~fabian/papers/fgcs.pdf