bio metrics & cryptography
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Cryptography
& Biometric
Identification
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Biometrics refers to the automatic identification of a person based
on his/her physiological or behavioral characteristics. This method
of identification is preferred over traditional methods involvingpasswords and PIN numbers for various reasons: (i) the person to
be identified is required to be physically present at the point-of-
identification; (ii) identification based on biometric techniques
obviates the need to remember a password or carry a token. With
the increased use of computers as vehicles of informationtechnology, it is necessary to restrict access to sensitive/personal
data. By replacing PINs, biometric techniques can potentially
prevent unauthorized access to or fraudulent use of ATMs, cellular
phones, smart cards, desktop PCs, workstations, and computernetworks.
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PINs and passwords may be forgotten, and token based methods of
identification like passports and driver's licenses may be forged,
stolen, or lost. Thus biometric based systems of identification are
receiving considerable interest. Various types of biometric systems
are being used for real-time identification, the most popular are
based on face, iris and fingerprint matching. However, there areother biometric systems that utilize retinal scan, speech, signatures
and hand geometry.
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A biometric system is essentially a pattern recognition systemwhich makes a personal identification by determining the
authenticity of a specific physiological or behavioral
characteristic possessed by the user. An important issue in
designing a practical system is to determine how an individual is
identified. Depending on the context, a biometric system can be
either a verification (authentication) system or an identification
system.
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Verification vs Identification:
There are two different ways to resolve a person's identity:verification and identification. Verification (Am I whom I claim I
am?) involves confirming or denying a person's claimed identity.
In identification, one has to establish a person's identity (Who am
I? ). Each one of these approaches has its own complexities and
could probably be solved best by a certain biometric system.
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Applications:
Biometrics is a rapidly evolving technology which has been widely used
in forensics such as criminal identification and prison security. Recent
advancements in biometric sensors and matching algorithms have led to
the deployment of biometric authentication in a large number of civilian
applications. Biometrics can be used to prevent unauthorized access to
ATMs, cellular phones, smart cards, desktop PCs, workstations, andcomputer networks. It can be used during transactions conducted via
telephone and Internet (electronic commerce and electronic banking). In
automobiles, biometrics can replace keys with key-less entry and key-
less ignition. Due to increased security threats, many countries have
started using biometrics for border control and national ID cards.
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ATM
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BenGurion Airport - Hand Geometry
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Fingerprint Matching:
Among all the biometric techniques, fingerprint-based
identification is the oldest method which has been successfully
used in numerous applications. Everyone is known to have
unique, immutable fingerprints. A fingerprint is made of a series
of ridges and furrows on the surface of the finger. The uniqueness
of a fingerprint can be determined by the pattern of ridges andfurrows as well as the minutiae points. Minutiae points are local
ridge characteristics that occur at either a ridge bifurcation or a
ridge ending.
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Fingerprint matching techniques can be placed into two categories:
minutae-based and correlation based. Minutiae-based techniques
first find minutiae points and then map their relative placement onthe finger. However, there are some difficulties when using this
approach. It is difficult to extract the minutiae points accurately
when the fingerprint is of low quality. Also this method does not
take into account the global pattern of ridges and furrows. The
correlation-based method is able to overcome some of thedifficulties of the minutiae-based approach. However, it has some
of its own shortcomings. Correlation-based techniques require the
precise location of a registration point and are affected by image
translation and rotation.
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Fingerprint Matching:
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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!). Toreduce 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.
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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. Fingerprintclassification 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. We have developed an algorithm to classify
fingerprints into five classes, namely, whorl, right loop, left loop,
arch, and tented arch.
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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 Gaborfilters. This information is quantized to generate a FingerCode
which is used for classification. Our 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. For thefive-class problem, classification accuracy of 90% is achieved.
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Fingerprint 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 reliesheavily 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 afingerprint enhancement algorithm in the minutiae extraction
module. We have developed a fast fingerprint enhancement
algorithm, which can adaptively improve the clarity of ridge and
furrow structures of input fingerprint images based on the estimated
local ridge orientation and frequency. We have evaluated theperformance of the image enhancement algorithm using the
goodness index of the extracted minutiae and the accuracy of an
online fingerprint verification system. Experimental results show
that incorporating the enhancement algorithms improves both the
goodness index and the verification accuracy.
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Hand Geometry:
This approach uses the geometric shape of the hand for
authenticating a user's identity. Authentication of identity using
hand geometry is an interesting problem. Individual hand features
are not descriptive enough for identification. However, it is
possible to devise a method by combining various individual
features to attain robust verification.
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Hand Geometry vs Fingerprints:
Unlike fingerprints, the human hand isn't unique. One can use
finger length, thickness, and curvature for the purposes ofverification but not for identification. For some kinds of access
control like immigration and border control, invasive biometrics
(eg., fingerprints) may not be desirable as they infringe on
privacy. In such situations it is desirable to have a biometricsystem that is sufficient for verification. As hand geometry is not
distinctive, it is the ideal choice. Furthermore, hand geometry data
is easier to collect. With fingerprint collection good frictional skin
is required by imaging systems, and with retina-based recognition
systems, special lighting is necessary. Additionally, handgeometry can be easily combined with other biometrics, namely
fingerprint. One can envision a system where fingerprints are used
for (infrequent) identification and hand geometry is used for
(frequent) verification.
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Face Retrieval:
The face retrieval problem, known as face detection, can be defined
as follows: given an arbitrary black and white, still image, find thelocation and size of every human face it contains. There are many
applications in which human face detection plays a very important
role: it represents the first step in a fully automatic face recognition
system, it can be used in image database indexing/searching by
content, in surveillance systems and in human-computer interfaces.
It also provides insight on how to approach other pattern
recognition problems involving deformable textured objects. At the
same time, it is one of the harder problems in pattern recognition.
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Face Retrieval:
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Integrating Faces and Fingerprints for Personal Identification :
An automatic personal identification system based solely on
fingerprints or faces is often not able to meet the systemperformance requirements. Face recognition is fast but not reliable
while fingerprint verification is reliable but inefficient in database
retrieval. We have developed a prototype biometric system which
integrates faces and fingerprints. The system overcomes the
limitations of face recognition systems as well as fingerprintverification systems. The integrated prototype system operates in
the identification mode with an admissible response time. The
identity established by the system is more reliable than the identity
established by a face recognition system. In addition, the proposed
decision fusion schema enables performance improvement by
integrating multiple cues with different confidence measures.
Experimental results demonstrate that our system performs very
well. It meets the response time as well as the accuracy
requirements.
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AMultimodal Biometric System Using Fingerprint, Face, and
Speech:
A biometric system which relies only on a single biometricidentifier in making a personal identification is often not able to
meet the desired performance requirements. Identification based on
multiple biometrics represents an emerging trend. We introduce a
multimodal biometric system, which integrates face recognition,
fingerprint verification, and speaker verification in making apersonal identification. This system takes advantage of the
capabilities of each individual biometric. It can be used to
overcome some of the limitations of a single biometrics.
Preliminary experimental results demonstrate that the identityestablished by such an integrated system is more reliable than the
identity established by a face recognition system, a fingerprint
verification system, and a speaker verification system.
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Cryptography
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Cryptography is the science of writing in secret code and is an
ancient art; the first documented use of cryptography in writing
dates back to circa 1900 B.C. when an Egyptian scribe used non-standard hieroglyphs in an inscription. Some experts argue that
cryptography appeared spontaneously sometime after writing was
invented, with applications ranging from diplomatic missives to
war-time battle plans. It is no surprise, then, that new forms of
cryptography came soon after the widespread development of
computer communications. In data and telecommunications,
cryptography is necessary when communicating over any
untrusted medium, which includes just about any network,
particularly the Internet.
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Within the context of any application-to-application
communication, there are some specific security requirements,
including:
y Authentication: The process of proving one's identity. (The
primary forms of host-to-host authentication on the Internet today
are name-based or address-based, both of which are notoriously
weak.)
y Privacy/confidentiality: Ensuring that no one can read the
message except the intended receiver.
y Integrity: Assuring the receiver that the received message
has not been altered in any way from the original.
Non-repudiation:A mechanism to prove that the sender really sent
this message.
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Cryptography, then, not only protects data from theft or alteration,
but can also be used for user authentication. There are, in general,
three types of cryptographic schemes typically used to accomplish
these goals: secret key (or symmetric) cryptography, public-key
(or asymmetric) cryptography, and hash functions, each of which
is described below. In all cases, the initial unencrypted data isreferred to asplaintext. It is encrypted into ciphertext, which will
in turn (usually) be decrypted into usable plaintext.
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TYPES OF CRYPTOGRAPHIC ALGORITHMS
There are several ways of classifying cryptographic algorithms.
For purposes of this paper, they will be categorized based on thenumber of keys that are employed for encryption and decryption,
and further defined by their application and use. The three types
of algorithms that will be discussed are (Figure 1):
y Secret Key Cryptography (SKC): Uses a single key forboth encryption and decryption
y Public Key Cryptography (PKC): Uses one key for
encryption and another for decryption
y Hash Functions: Uses a mathematical transformation to
irreversibly "encrypt" information
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Secret Key Cryptography
With secret key cryptography, a single key is used for both
encryption and decryption. As shown in Figure 1A, the sender usesthe key (or some set of rules) to encrypt the plaintext and sends the
ciphertext to the receiver. The receiver applies the same key (or
ruleset) to decrypt the message and recover the plaintext. Because
a single key is used for both functions, secret key cryptography isalso called symmetric encryption.
With this form of cryptography, it is obvious that the key must be
known to both the sender and the receiver; that, in fact, is the
secret. The biggest difficulty with this approach, of course, is the
distribution of the key.
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Secret key cryptography schemes are generally categorized asbeing eitherstream ciphers orblock ciphers. Stream ciphers
operate on a single bit (byte or computer word) at a time and
implement some form of feedback mechanism so that the key is
constantly changing. A block cipher is so-called because the
scheme encrypts one block of data at a time using the same key on
each block. In general, the same plaintext block will always
encrypt to the same ciphertext when using the same key in a block
cipher whereas the same plaintext will encrypt to different
ciphertext in a stream cipher.
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Stream ciphers come in several flavors but two are worth
mentioning here. Self-synchronizingstream ciphers calculate
each bit in the keystream as a function of the previous nbits in
the keystream. It is termed "self-synchronizing" because the
decryption process can stay synchronized with the encryption
process merely by knowing how far into the n-bit keystream it is.
One problem is error propagation; a garbled bit in transmission
will result in n garbled bits at the receiving side. Synchronousstream ciphers generate the keystream in a fashion independent
of the message stream but by using the same keystream
generation function at sender and receiver. While stream ciphers
do not propagate transmission errors, they are, by their nature,
periodic so that the keystream will eventually repeat.
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