Designing a Multi-Biometric System to
Fuse Classification Output of Several
Pace University Biometric Systems
Leigh Anne Clevenger, Laura Davis, Paola Garcia Cardenas,
Onenetta Labeach, Vinny Monaco and James Ng
Seidenberg School of Computer Science and Information Systems
Pace University, White Plains, NY
Student/Faculty Research Day, May 2, 2014
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Abstract
• Some high-level biometric systems combine, or fuse,
several biometrics to increase performance over that of
an individual biometric system. This project investigates
how to combine, at the classification output level,
several Pace University biometric systems.
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Overview
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Introduction
Multi-Biometric Fusion
Pace University Biometric Systems
Methodology
Proposed Fusion Approaches
Summary of Results
Conclusions
Future Work
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Introduction
Enhancement of Security Systems
• 2008 Higher Education Opportunity Act
In order to verify the identity of students taking online tests, the U.S
Government enacted this act requiring colleges and universities to
implement stricter control technologies for online systems
• There are different traits for biometric systems:
• Physical traits
Face, fingerprint, iris, retina, hand geometry, hand vein, palm print,
DNA, and teeth.
• Behavioral traits
Voice, signature, gait, keyboard typing patterns, and mouse
movements
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Introduction
Enhancement of Security Systems
• Biometric security requirements from Jain et al. include:
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Universality – each person has characteristic
Distinctiveness – characteristic different between two persons
Permanence – characteristic invariant over time
Collectability – characteristic measurable
Performance – analysis speed of biometric system
Acceptability – people will tolerate use of system
Circumvention – how easily can the system be fooled
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Introduction
Why a Multi-Biometric System is Needed
• To enhance overall performance of multiple biometric
traits over the performance of an individual trait
• To identify online test takers authentication more
efficiently
• Usage of behavioral traits:
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Continuous authentication
Non-Intrusive
Cost-Effective
Process authentication over networks
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Multi-Biometric Fusion
Literature Review
Researchers have applied:
• General vector fusing algorithms at the
• Feature level
• Score fusing algorithms at the
• Classification level
Feature classification data are vectors of biometric input data.
These vectors are combined by a classifier to produce score
output used for authentication.
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Multi-Biometric Fusion
Block diagram for a generic multi-biometric system using
classification output level fusion
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Pace University Biometric Systems
• Pace University Biometric System (PBS)
• Pace University Keystroke Biometric System (PKBS)
• Pace University Systems used in Online Testing
• Pace Classifier and Simulated Authentication Process
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Methodology
• We investigated many fusion approaches
• An accepted fusion approach would:
• Analyze our PBS score output data from online testing sessions
• Work with data from sources other than online testing
sessions, for example Android data
• We narrowed the approaches down to just a few for our
experimental purposes
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Methodology
Experimental Data
• Four different biometric traits were collected by Pace
University’s existing frontend system and backend classifier:
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Mouse Motion
Click
Scroll
Keystroke
• Outputs from online examinations
• 14 subjects taking 10 examinations each with 10 questions
and 20 minutes duration. Sample (m) data of keystroke, click,
motion and scroll were actively accumulated from each
session.
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Methodology
Classification Results
• FAR of the click trait increases as the sample data (m)
increases. FRR on the other hand, decreases as more samples
are added
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Methodology
Classification Results
• The keystroke FRR and FAR results are similar to the click
results; however the FRR seem to be higher when there are
100 samples and only starts decreasing significantly when it is
close to the 200 samples.
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Methodology
Classification Results
FRR line
• The motion error rate is one of the traits with the better
results. When up to 50~60 samples are used, the FRR and FAR
are very low. The FAR only starts increasing significantly after
200 samples.
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Methodology
Classification Results
• This graph shows how the scroll trait is very inconsistent. It is
believed that this phenomenon happens due to the fact that
users do not always interact with the scrolling functionality,
forcing the inconsistency of the graph.
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Methodology
Classification Results
Motion ROC
• In the ROC curve we can see the FAR and FRR percentages for
all the four traits. Motion trait is the closest to zero.
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Proposed Fusion Approaches
Chair-Varshney Decision Fusion Rule
• The local decisions (n) are combined to obtain a global
decision; this combination is done by a decision fusion center
(DFC):
• When this rule was applied in “Decision Fusion for Multimodel
Active Authentication,” using the parallel decision fusion
architecture, the global decision returned better results (lower
error rate) than that of an individual decision performing at its
best on its own -which is exactly the type of results we are
trying to obtain for this research.
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Proposed Fusion Approaches
Six Classifier Fusion Strategies
• These six classifier fusion strategies are theoretically studied
by L. Kuncheva, and they are as follows:
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Minimum
Maximum
Average
Median
Majority Vote
Oracle
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Summary of Results
• Motion and keystroke biometric traits were the top two
biometrics traits with the best performance in these studies
• Simply concatenating the feature vectors from each biometric
yielded an EER of 3.9%. This is worse than the single best
performer, mouse motion
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Conclusions
• Motion data was the most accurate biometric trait / Scroll
data was the most inaccurate due to different factors.
• Proposed methods of examination for classification output
level fusion:
• The Chair-Varshney decision fusion rule approach
• The six classifier fusion strategies (minimum, maximum, average,
median, majority vote and oracle)
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Future Work
• Application of the proposed classification output fusion
methods compared with the single measurement
performances of the four individual biometric traits
• Comparison to previous EER results
• For any of these methods to be successful, we will need to
yield an EER of no more than 1.139%, which is just 0.001% less
than the EER of our most successful single biometric trait,
motion
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Questions?
THANK YOU FOR YOUR ATTENTION!
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Methodology
Classification Results
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Pace University Biometric Systems
Pace University Biometric System (PBS)
• PBS has a generic, robust backend system designed to
accommodate various biometric frontend feature inputs
like keystroke, mouse, stylometry and voice
• PBS relies on databases of approved signature templates
that were enrolled and stored earlier
• PBS analyses the signature inputs against the approved
data stored on the template database to verify identify
and authenticate users
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Pace University Biometric Systems
Pace University Keystroke Biometric System (PKBS)
• PKBS consists of two logging components and a single
backend for extracting features and classification
• Web-based loggers are loaded into any webpage and
captures all keystroke and mouse movements, while
buffering and transmitting them to a server
• Cross-platform native logger is written in Java
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Pace University Biometric Systems
Pace University Systems used in Online Testing
• A no-hurry system is used to authenticate user
• All keystroke data from the online testing is used
• To detect unauthorized users the system has to act in a
minute or less before any harm is done
• Keystroke data is captured using a Java applet that uses
the PC Windows clock to record key press and release
times in millisecond format
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Pace University Biometric Systems
Pace Classifier and Simulated Authentication Process
Pace Classifier
• Implements the kNN (k-Nearest Neighbor) classifier
• Used to transform a multi-class (polychotomy) problem into a
dichotomy model, this is, a problem that only involves two classes.
These classes are:
• within-person (authenticated)
• between-person (not authenticated)
Simulated Authentication Process
• User’s keystroke sample requiring authentication is first converted
into a feature vector. The difference between this feature vector and
an earlier-obtained enrollment feature vector from this user is
computed, and the resulting difference vector is classified as one of
the two previous classes.
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