Multi-factor Implicit Biometric Authentication: Analysis and Approach

MULTI-FACTOR IMPLICIT BIOMETRIC AUTHENTICATION 1
Multi-Factor Implicit Biometric Authentication: Analysis and Approach
Jigisha Aryya
Illinois Institute of Technology, Chicago
Author Note
Firstly, I thank the institute for providing a rich repository of scholarly articles and material for
carrying out this research submitted on this day of November, 2016. Any questions about this paper
should be sent through email at jaryya@hawk.iit.edu I thank Prof. Raymond E. Trygstad for
suggesting improvements to this work. Second, you are hereby granted permission to use (and adapt)
this document for learning and research purposes. You may not sell this document either by itself or in
combination with other products or services. Third, if you use this document, you use it at your own
risk. The document’s accuracy and safety have been thoroughly evaluated, but they are not guaranteed.

Abstract
The online world is ever-growing and generates data that is valuable and needs to be
protected. With the advent of advanced technologies like IoT, smartphones, bigdata etc. it becomes a
responsibility of any government or organization to create protocols that will protect the information
exchanged by millions of devices. Authentication being the first step in restricting access to sensitive
information and data, it is important that the processes, technologies and policies for this are modified
as per the changing needs. Biometrics has a promising future. However, there are challenges like
operational feasibility, user acceptance, technical problems like mobile device resource limitations and
concern over protecting the users' personal data, that have motivated researchers to look for more
efficient and viable techniques.
Continuous implicit biometric authentication is a process of correctly identifying users
by collecting data about their behavior over a period of time and processing it using Machine Learning
algorithms, Evaluation Matrices etc. This is contrary to physiological biometrics that only uses the
physical attributes.
We look in detail the current scenario and research in physiological and continuous
implicit biometric authentication technique and its practical applicability in various sectors and discuss
the challenges that they pose and ways of overcoming them.
Keywords: authentication, implicit biometrics, behavioral traits, physiological biometrics, user
acceptance, operational feasibility

Multi-Factor Implicit Biometric Authentication: Analysis and Approach
In the current digital world, most of the business and personal transactions, as well as
organizational information sharing and storage happens over a network of connected systems, be it a
cloud storage devices, smartphones or home appliances. A person or an entity is authenticated
commonly by using a login ID and password combination in order to be given access. Cyber criminals
and hackers have already figured out ways of cracking these secret credentials using phishing and brute
force techniques in order to steal or manipulate the assets that are available and accessible through the
network. On the other hand, cyber security experts, researchers and personnel are continually looking
for ways to stop these incidents from happening. Biometrics that use what the entity possesses for
determining the identity, is not a new concept but hasn't yet been adopted widely. Only in recent times,
commercial institutions like banks that handle sensitive information are looking to this method of
authentication due to its promising future. However, over time many drawbacks have been identified
that are still in the process of being resolved. Also, newer techniques are being experimented with to
counter the imminent threats. In the following sections we will see the distinct characteristics and
drawbacks of the popular techniques that are being considered world-wide and then analyze possible
solutions that might solve these issues.
Physiological versus Behavioral (Implicit) Biometrics – Implementation and Challenges
Physiological Biometrics like fingerprint authentication is being considered for widespread use
in various banks like Bank of America, Royal Bank of Scotland, HSBC etc. S.T. Bhosale and Dr. B.S.
Sawant (2012) have documented the way fingerprint authentication can lead to “cardless” ATM
transaction. Various computing and mobile devices are equipped with the hardware that is required to
scan the fingerprint of its owner and store it for future authentication. But not all devices like ATMs
widely used support this facility. The usual way of restricting the access to these devices is a PIN or a
password encrypted and stored in the system files or sent to a remote server for authentication. Same is

the case with the other physical attributes like face, retina, iris, palm vein etc. which are in fact even
more niche and rarely seen used in the consumer market or any organization that stores sensitive
information. What is hindering the presence of these technologies is the current state of the hardware
manufactured and used in the devices with which the common consumer interacts like the smartphones,
cash machines, PCs, POS, Kiosks, ATMs, home appliances connected via Internet (IoT) etc. But as
rightly pointed out by Peter Corcoran and Claudia Costache (2016), many a times it is just not possible
for system designers to efficiently incorporate a module for biometric authentication in a device due to
its complexity. The companies that are selling products with which its users might transmit or store
sensitive information, will have to take the necessary steps to enable the penetration of biometrics into
the consumer market. Along with that the storage of the information collected from the users that is
irreplaceable, unlike a password will have to be transmitted to the servers if required and stored as
securely as possible as Peter Corocon et al have rightly quoted “A key problem with biometrics is that
they cannot be revoked”. These are the first and foremost requirements for rapid adoption of
physiological biometrics authentication. Yana Welinder (2016) has mentioned “They will nevertheless
get hacked”. Dr. Thomas P. Keenan in his article has spoken about several serious security breach
possibilities that continue to haunt this technology. So, detection of security bypasses and immediate
remedies for replacement of the unique identity information is also crucial. Second comes
understanding the way these repeated actions of authentication either at the system access level or
application access level weigh on the users, so that they agree to use them for their online security.
Battery power consumption, time of completion of the authentication process, prevention of misuse,
loss or corruption of data etc. are the factors that come into play in this case ( intensive technical
analysis by Paolo Gasti et al, 2016). We will discuss the possible ways by which these issues can be
addressed by the current research in this area similar to what has been proposed by Jigisha Aryya
(2008) and Paolo Gasti et al. (2016).

Behavioral or Implicit biometrics on the other hand, collects data that reflect the way the user
interacts with the system. Voice authentication is being adopted by Banks like Singapore Bank,
Barclays Capital, Citi Bank etc. Keystroke dynamics, mouse dynamics, location information and
touchscreen interaction are ways of identifying a user while they are either aware or unaware of the fact
that they are giving behavior specific identity information for their authentication. Similarly, gait, hand
waving, signature etc. Some of these are relevant only to mobile devices like gait and touchscreen as of
now. However, again, authentication only at the entry point of a system is not enough and should be a
continuous process to ensure complete integrity. This is where continuous behavioral biometrics
authentication takes over. But, doing so can be costly again in terms of user experience and resource
consumption. With a sophisticated design this can be handled well. Abdulaziz Alzubaidi and Jugal
Kalita in their research on the various methods compares the uniqueness and challenges very well and
dives deep into the technicalities. Their work has influenced this paper to a large extent. But as their
work is more focused on mobile devices used by the common consumers, only a part of it has been
picked for further analysis. We will try to analyze the methods and possible better solutions that can be
implemented at a faster and cheaper rate.
The Challenges with Physiological Biometrics and Possible Solutions
Securing the templates
The face impression, fingerprint, iris image, retina or palm print can never be changed and hence used
for identifying a person. And it is always present with the person, meaning it is unlike a password or a
PIN that could be forgotten or become invalid. It is also quick to provide. While this gives an edge to
biometric security systems, it also means that special precautionary measures are needed to store the
templates for comparison and as securely as possible. For any large organization, it is not difficult to
provide machines for identity verification at various points whether online or physical access. These
machines sometimes can capture the impression easily, for example, at a security check point of an

airline where iris verification is done for international travelers. The camera has to align with the eyes
and then take the picture to compare with the already stored iris template. These machines could be
connected to a remote server that receives the template as soon as it is taken and then sends the result.
Additionally they can be encrypted after converting them to bytes of data or obfuscated and again made
right with a known algorithm and key at the server side. This can prevent identity theft. On the other
hand, if we choose to store it locally like in a smartphone or a laptop or desktop, then the operating
system of that machine has to locally encrypt this data and then store at a safe section of the hard disk.
Theft with fake sources like videos can be prevented with liveness check like pupil contraction with
exposure to light. The fast exchange of data between the local computer and the server and processing
speed is the key to its success be it iris, fingerprint or face.
Technical feasibility and User acceptance
For a terminal authenticating a person requesting access, it is easy to set up a system that will have the
hardware to get the impression of the eye, face or palm etc. correctly. But for an online access, it is not
always convenient or feasible to get the picture correctly specially of the face or iris/retina since the
camera resolution might not be of good enough or the user might not be able to align the eye or face
correctly. As such, the authentication system will not get an accurate picture to test. Hence, most
computers and mobile phone have at the most, palm or fingerprint authentication. Unless the hardware
is improved and proper system-generated feedback to the user is not given to align properly, a useful
picture cannot be taken. This might although annoy the person being authenticated, and so, it is best to
use an alternative attribute that is easier to collect. Behavioral biometrics have an upper hand in such
scenarios. Many a times the user is unaware since the interaction itself functions as the “password”.
Since in the remote server scenario, authentication is done by communication over a network, it is
important to keep in mind chances of server and network failures with designing the system and ways
of handling such incidents. Server images containing replica of the templates in a different network can

help in cases of technical failures like these. Cloud based systems are also feasible as described by
Salman H. Khan et al. (2015).
Privacy Issues
For any organization to authenticate a personnel at all required check points is acceptable only
if an individual agrees to be tracked with physical data whenever required and continuously if so. Same
is the case with any commercial application. But since this data is private and permanent, a person
might feel uncomfortable being tracked of his or her location and activities. It is important that
surveillance area are marked properly and the videos and data captured are only accessible to
authorized personnels. If any other person manages to get the access and decides to misuse it, the
reputation of the organization will be spoiled irreversibly. The government or the organization should
keep levels of access for the identification of an individual when using biometric data like face
recognition for security purposes. Managers, Security experts etc. have to be labeled and privileges
identified for getting access to this data sensitively. Access controls to designated areas containing this
information need to be defined as Mandatory Access Controls (MACs) or Non-discretionary controls
that are decided by the top executives and independent advisors and are role based.
The Challenges with Implicit Biometrics and Possible Solutions
Diversity of application
The behavior of an individual like voice, keystroke, touchscreen etc. are very specific and can
be collected mostly with mobile devices accurately. The gait for example cannot be used any other
fixed form of hardware system since it uses the movement of the person. Voice is the most diverse of
all since any machine with a microphone and CPU can capture or process audio data. Keystroke and
touch behavior are also feasible if the device is equipped with a keypad and touch sensitive surface
which is very common in smartphones. Hardware collecting fingerprints is different from touch based
surfaces. But touch interaction would typically require a bit more time to correctly identify a user. That

makes it a trait to be tracked only in smartphones where the user is using it continuously so that
training data can be collected for feeding into a machine learning software system. Same is the case
with keystroke. Other traits like hand-waving and signature are comparatively convenient. Cameras and
optical surfaces that have the ability to capture pen movement are apt for these features. Hence, in
terms of implementation at a physical security check terminal, behavior might pose to be a challenge
except for voice input. Gait could be tracked with surveillance cameras from a certain distance till the
final check point. But again that would also require identifying parallely with face recognition.
Device resource limitations
We know that smartphones and small smart appliances have resource limitations like battery life
and network bandwidth. Authenticating continuously or frequently can levy heavily on it. Researchers
now are working on the core technicalities of the software for making it efficient. Algorithms are
modified with better mathematical techniques. Paolo Gasti et al have given optimized Manhattan and
Hamming distance calculation formula. Compressing the data is crucial to make the authentication
process leaner.
Privacy Issues
Just like physiological traits, behavioral information is also unique and needs to be protected
from cyber criminals. More importantly, it should be informed to the user unless and until the
sensitiveness is not too high or the demand is such.
Adapting to Changing Behavior
A system that authenticates using behavioral features needs to be aware that these
characteristics might change with age and ailment more rapidly than physical attributes. As such, the
software has to be intelligent enough to adapt to the changes. Artificial Intelligence along with machine
learning algorithms can help to a large extent. Only concern is economic and operational feasibility
since building, deploying and maintaining such a system might be costly.

A Hybrid Approach
It is important to understand the pros and cons of each type of biometrics before deciding on its
implementation and design. Cost, effort, time frame and organizational impact depending on the type of
entities to be authenticated are factors that need to be considered. For a robust and futuristic design that
will hold good for atleast sometime, it is a good approach to combine both the types and create a hybrid
authentication system. We know that any institution like a bank or a government organization, access to
information and assets happen at different times and under different conditions depending on its
criticality.
There are basically two levels at which authentication is desired: Entry level and Interaction
level. For physical systems, physiological biometrics like fingerprint, palm print or iris verification is a
quick and easy method. After getting access, the person's movement, face etc. can be monitored till a
certain time to completely ensure that it is indeed the authenticated person. In digital systems that are
mobile, it is possible to have physiological or behavioral biometrics at entry level followed by
continuously authenticating using behavioral traits . It is also feasible and convenient to have a
complete behavioral biometric security system. UnifyID is one of those technological startups that are
working to create products to completely replace any other methods with implicit biometrics for initial
and continuous authentication. However, this is focused only on smartphones, which is not always the
case and the main assumption is that, it is being used only by a single user. Hence, the ideal system has
to adapt to the device size and underlying system architecture. Personal computers that are used to
access electronic portals can be very basic that might not even have a working microphone facility or a
camera. In that case, only implicit biometrics like keystroke dynamics has to be the extra input apart
from some traditional techniques like OTP or a password. Bakelman et al have carried out experiments
of various categories of passwords and keyboard inputs that could be also used for keystroke type of

implicit authentication which is very valuable in terms of creating a more realistic and strong cyber
security system.
For computers and mobile devices having hardware to support audio and video input, face
recognition, handwaving and voice recognition techniques can be used. There are also computers that
have inbuilt hardware for detecting the fingerprint. Smartphone operating systems that support
fingerprint detection also are slowly penetrating the consumer markets. Mouse movement in computers
is equivalent to touchscreen dynamics in smartphones which is a behavioral characteristic. Gait is
specific to smartphones. In the figure below we try to list the different types based on their
implementation ease and practicality.
Entry level → Interaction level
Keystroke dynamics (Implicit) Mouse / Touchscreen interaction (Implicit)
Voice recognition (Implicit) Keystroke dynamics (Implicit)
Face recognition (Physiological) Voice inputs (Implicit)
Hand-waving (Implicit) Gait (Implicit)
Iris/Retina recognition (Physiological)
Fingerprint recognition (Physiological)
Palm print recognition (Physiological)
Signature (Implicit)
Continuous Implicit Biometrics
It is known well the details of physiological biometrics. But to implement a continuous
biometric authentication system that collects and sends the biometric data to a server for learning or
verification using matrices, it is important to evaluate the hardware and software implicates. It is

possible to authenticate digitally end to end using implicit techniques, without the person having to
consciously enter any identity information. But initial identity capturing has be through the hardware as
mentioned above to create the training set or evaluation matrices of values that will be used later for
comparison. This is the future of biometrics and authentication as a whole but if the system fails to
efficiently identify the data, it might lead to login failures, system locks, corruption of behavior data
and overall user dissatisfaction. For digital systems this looks feasible and specially in mobile devices.
For physical assets like storage devices and servers, continuous process might be challenging in terms
of quick implementation. Target Mimicry attacks and Reconstruction attacks as have been studied by
J. Morris Chang et al and Daniel Vogel et al are a reality and need to tackled with great precision. As
the user continues to use a system, even with long gaps of use, the system has to build and modify the
data in order to accommodate any changes in the behavior due to aging. Also special cases when the
actual user isn't being able to interact normally, the system should be intelligent enough not to deny
access to the user. It is always good to have a standby system of alternate authentication in case of such
failures. This is to ensure we do not frustrate the user by causing a great deal of inconvenience.
Looking Beyond Biometrics - Mindmetrics
It is interesting to see that those who are inclined to contribute to the field of cyber security in a
more practical ways have always started with the ease of development and deployment of the
technology. There are researchers who have proposed determining the correct users by testing their
personality with a desired one, which is very similar to psychometric tests. This is often clubbed with
behavioral biometrics like keystroke dynamics to increase its strength. The end user requesting access
is asked certain questions, the answers to which are expected only from the right user. In fact, entering
password is completely eliminated and login id is asked for only later from a set of choices. This design
proposed by Juyeon Jo et al (2014) has been developed using a simple interface coded using an
application programming language. The user feedback was good as per their user acceptance tests. This

is not a solid proof of its feasibility at a large scale but it definitely indicates that there is a new
possibility and direction in biometric authentication that is not only easy to implement but also tests the
person using a more complex parameter which are private information or psychology.
A Complete Biometric based Security System
An organization like bank which is the foremost in implementing biometrics has several divisions,
products and personnels with varied roles who access and modify the data related to the capital
managed by the bank. In the figure below we try to visualize all the possible places where biometrics
could be used in a multi-modal fashion.
 Account access by Customer through a Bank branch
 Account access by Customer through an ATM
 Account access by Customer through a Web portal using a browser
 Account access by Customer through a Smartphone Application
 Bank employee's access to customer data
 IT Manager's access to servers containing sensitive data of customers
 DBA's access to bank databases
 IT Infrastructure personnel's access to network servers and storage devices like cloud servers
 Technical team's access to software governing the customer and bank data
 Areas in the bank's corporate offices where software development and data maintenance
takes place

These are just a few name. There are several other repositories of data containing financial reports and
fund management data that need very secure handling. Biometrics can either make or break the security
system depending on how it is understood and implemented.
Managerial Precautions in Launching the Advanced Biometrics
It is always advised to follow the TAM (Technolody Adoption Model) to introduce some new
technology into the system. The acceptance has to be tested no matter how ambitious and futuristic it
is. People being authenticated several times in several different ways can have dire consequences if it
comes as a surprise. But at the same time the management has to convince the employees and the
customers equally of its criticality. Policies governing the tapping of user biometrics data and analyzing
the same for some useful insights also need serious consideration in case it leads to legal issues
claiming breach of privacy rights of individuals. The security aspect must not become a hindrance in
the normal business processes and so an investment in this area requires considering failure scenarios.
The cyber security insurance domain is the newest upcoming area of innovation and advancement as
security can lead to disastrous consequences due to external attacks or internal issues. This makes it
worth the investment necessary. Optimizing the system in terms of the selection of the factors that will
be authenticated against, the management can come up with something that is feasible in terms of cost
and acceptance. As surveyed by Licky Richard Erastus et al., an Emerging Market might not accept an
extensive biometrics based security system as easily as a developed economy. Hence, pilot projects
with these methods is very much advised.

Conclusion
There are several benefits and challenges in using physiological and implicit biometrics. Continuous
implicit biometrics authentication combining two or more factors is the future. If implemented in a
sophisticated manner it can lead to not only higher security assurance but also better user experience.
However, if we fail to address the drawbacks that it suffers from, like precision and efficiency needs, it
can lead to disastrous results in terms of access and system performance. We see in multi-factor
authentication that it is indeed wise to combine physiological biometrics with implicit or behavioral
biometrics rather than depending on just one type. This along with adaptive algorithms implemented at
the low level software design can make a pure biometrics based authentication not only robust but also
a means of improving user experience. What factors need to be used depend on the system architecture
in question. Face, fingerprint, voice recognition and keystroke dynamics along with iris recognition
look popular and accepted so far in sectors like banking. We need to explore more on touchscreen
dynamics since smartphones and mobile devices are the future of information access.

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