Generate a key for MAC Algorithm using Biometric Fingerprint

International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.1, No.4, December 2010
DOI : 10.5121/ijasuc.2010.1404 38
Generate a key for MAC Algorithm using
Biometric Fingerprint
Dr.R.Seshadri1
and T.Raghu Trivedi2
1
Director, University Computer Center, S.V. University, Tirupathi, Andhra Pradesh,India.
ravalaseshadri@gmail.com
2
Research Scholar, University Computer Center, S.V. University, Tirupathi, Andhra
Pradesh,India.
tamirisa_t1@yahoo.com
Abstract
One of Authentication technique involves the use of a secret key to generate a small fixed size block of data
known as cryptographic checksum or MAC that is appended to the message.
The unauthorized thefts in our society have made the requirement for reliable information security
mechanisms. Information security can be accomplished with the help of a prevailing tool like cryptography,
protecting the cryptographic keys is one of the significant issues to be deal with. Here we proposed a
biometric-crypto system which generates a cryptographic key from the Finger prints for calculating the
MAC value of the information we considered fingerprint because it is unique and permanent through out a
person’s life.
Key words MAC, Fingerprint, Encryption, Decryption, Minutiae point, ROI.
1. Introduction
Message authentication code is a public function of the message and a secret key that produces a
fixed length value that serves as the authenticator. Protecting the secret key is major issue.we are
going to use the biometrics for generating /protecting the secret key. A fingerprint is made of a
number of ridges and valleys on the surface of the finger. Ridges are the upper skin layer segments
of the finger and valleys are the lower segments. The ridges form so-called minutia points: ridge
endings and ridge bifurcations. Many types of minutiae exist, including dots. The uniqueness of
a fingerprint can be determined by the pattern of ridges and furrows as well as the minutiae points.
There are five basic fingerprint patterns: arch, tented arch, left loop, right loop and whorl. Loops
make up 60% of all fingerprints, whorls account for 30%, and arches for 10%.Fingerprints are
usually considered to be unique, with no two fingers having the exact same dermal ridge
characteristics. Here we use Novel method of biometrics based key generation technique.
Biometric crypto systems can operate in one of the following three modes 1.Key Release 2.Key
binding 3.Key generation. Here we use the key generation mode in which the key is derived
directly from the biometric data and is not stored in the data base.
2. MAC
This is one of the message authentication techniques. This technique assumes that two
communicating parties, say A,B share a common secret key k when A has a message to send to B,
it calculates the MAC as a function of the message and the key MAC=Ck((M). The message plus
MAC are transmitted to the intended recipient. The recipient performs the same calculation on the

39
received message, using the same secret key, to generate a new MAC. The received MAC is
compared to the calculated MAC.
If we assume that the receiver and the sender know the identity of the secret key. If received MAC
matches the calculated MAC then receiver is assured that message has not been altered and the
message is from alleged sender. MAC functions similar to encryption one difference is MAC
algorithm need not be reversible , as it must for decryption .To provide confidentiality message is
encrypted before the MAC algorithm .Here we need two separate keys, each of which shared by
sender and receiver [1] .
Figure1.Encryption and Decryption
3. How does fingerprint biometrics work
Maintaing and sharing the lengthy keys is critical problem. This can be overcome with the help of
biometric system [2].Here we will use finger print.
The main technologies used to capture the fingerprint image with sufficient detail are optical,
silicon, and ultrasound. There are two main algorithm families to recognize fingerprints:
K2
C
Compare
D M
K2
EK2[M]
CK1EK2[M]
K1
Receiver
K1
M ||
M
C
EK2[M]
CK1EK2[M]
Sender

40
Minutia matching compares specific details within the fingerprint ridges. At registration (also
called enrollment), the minutia points are located, together with their relative positions to each
other and their directions. At the matching stage, the fingerprint image is processed to extract its
minutia points, which are then compared with the registered template.
Pattern matching compares the overall characteristics of the fingerprints, not only individual
points. Fingerprint characteristics can include sub-areas of certain interest including ridge
thickness, curvature, or density. During enrollment, small sections of the fingerprint and their
relative distances are extracted from the fingerprint. Areas of interest are the area around a minutia
point, areas with low curvature radius, and areas with unusual combinations of ridges.
Key vector Is formed based on minutiae points (ridge ending and ridge bifurcation)
4. Generating key vector from finger print
Step1: Capture finger print image
Step2: Minutiae points’ extraction.
Step3: Key vector generation.
For key generation we need following concepts
4.1 Minutiae Points Extraction from Fingerprints
It is supposed that fingerprints are distinct across individuals and across the fingers of a particular
individual [3]. Minutiae points are feature points extracted from a raw fingerprint image
A fingerprint can be defined as a pattern of ridges and valleys on the tip of the finger. A fingerprint
is therefore described by the distinctiveness of the local ridge features and their relationships.
Minutiae points denote these local ridge characteristics that appear either at a ridge ending or a
ridge bifurcation. The point where the ridge comes to an abrupt end is known as ridge ending and
the ridge bifurcation is denoted as the point where the ridge divides into two or more branches [3].
Minutiae points’ extraction involves
• Segmentation
• Image Enhancement
• Minutiae Extraction
4.1.1Segmentation
The first step in the minutiae points’ extraction is segmentation. The input fingerprint image is
segmented from the background to actually extract the region comprising the fingerprint.
Segmentation of an image represents the division or separation of the image into regions that have
similar attributes. At first, the image is pre-processed. The pre-processing phase includes
histogram equalization and Median filter. The pre-processed image is divided into blocks and
segmentation is carried out.

41
4.1.1. a Histogram Equalization
Histogram equalization amplifies the local contrast of the images, particularly when they are
represented with very close contrast values. Intensity is distributed through the histogram with the
aid of this regulation. Histogram equalization converts the input image so that out put image
comprises a uniform distribution of intensities The histogram, after the histogram equalization
transforms all the range from 0 to 255 which results if enhanced visualization effect [4].
Figure2. Fingerprint image before and after Histogram Equalization
Median filter is a non-linear digital filtering methodology frequently employed to eliminate noise
from images or other signals.
Preprocessed fingerprint image is divided into non-overlapping blocks of size 16x16 followed by
the calculation of gradient of each block. The standard deviation of gradients in X and Y direction
is computed and summed. Eventually, the resultant value is compared against a threshold value. If
it is greater than the threshold value the block is filled with ones, otherwise the block is filled with
zeros.
Fig3.Segmentation
4.1.2Minutiae Points Extraction
Minutiae points are extracted as follows:
• Binarization
• Morphological Operations
• Minutiae points’ extraction

42
Initially, the enhanced image is binarized. After binarization, morphological operations are
performed on the image to remove the obstacles and noise from it. Finally, the minutiae points are
extracted using the approach discussed.
4.1.2.1Binarization
The binary images with only two levels of interest: The black pixels that denote ridges and the
white pixels that denote valleys are employed by almost all minutiae extraction algorithms. A grey
level image is translated into a binary image in the process of binarization, by which the contrast
between the ridges and valleys in a fingerprint image is improved. The grey-level value of every
pixel in the enhanced image is analyzed in the binarization process. Then, the pixel value is set to a
binary value one when the value is greater than the global threshold, or else a zero is set as the
pixel value.
Fig4. Finger print before and after binarization
4.1.2.2 Morphological Operation
This eliminates any obstacles and noise from image, later it removes unnecessary spurs, bridges
and line breaks
Then thinning process is performed to reduce the thickness of the lines so that the lines are only
represented except the other regions of the image. Clean operator, Hbreak operator, Spur operator
and Thinning are the morphological operators applied.
Figure5.Morphological operation

43
4.1.2.3 Minutiae points’ extraction
Ridge Thinning is to eliminate the redundant pixels of ridges till the ridges are just one pixel wide.
uses an iterative, parallel thinning algorithm. In each scan of the full fingerprint image, the
algorithm marks down redundant pixels in each small image window (3x3). And finally removes
all those marked pixels after several scans. . Marking minutia point is relatively easy after
thinning. For each 3X3 window, if the central pixel is 1 and has exactly 3 one-value neighbors,
then the central pixel is a ridge branch. if the central pixel is 1 and has only 1 one –value
neighbors , then the central pixel is ridge ending. Suppose both the uppermost pixel with value 1
and the rightmost pixel with value 1 have another neighbor outside the 3X3 window, so the two
pixels will be marked as branches too. Actually only one branch is located in the small region. So a
check routine requiring that none of the neighbors of a branch are branches is added.
Figure6.Minutiae points
False Minutia Removal
False ridge breaks due to insufficient amount of ink and ridge cross-connections due to over inking
are not totally eliminated. This false minutia will significantly affect the accuracy of matching if
they are simply regarded as genuine minutia. So some mechanisms of removing false minutia are
essential to keep the fingerprint verification system effective.
Key vector generation
Using above discussion the Key is generated in following process
The minutiae points extracted from the fingerprint image are represented as follows
Mp=Minutiae point set
N= number minutiae points
Mpi ( xi , yi) = ith minutiae point with x,y Co-ordinates

44
Mp1
.
.
.
Mp1
J=1….m Pi ≠ Pj
Distance between two points pi, pj are calculated as
√ (xi -xj)2
+(yi - yj)2
After calculation of the distances the values are stored in vector as
D1
D2
Dn
Sort the above vector name it as V
V = [ v1 , v2 . . . vn ]
Now V is divided into two equal parts
Va = [ v1 , v2 . . . vn/2]
Vb = [ vn/2+1 , . . . vn]
Va and Vb are shuffled and stored in Sa and Sb
Sd = Sa U Sb
Convert the Sd into a matrix form of size √n/2 * √n/2 and name it Md
IKV = {Ki : P(K)}, i =1,...., |Sd|
(P1,Pj)
(P2 ,pj)
.
(pn,pj).
d11 . . . d1m
d21. . . d2m
dn1 . . . dnm

45
Where
P(k) = | SMij | mod 2,
SMij = Md i, j : i +size, j+ size, -1 <i, j <√Sd
5. Conclusion
Document transmission between systems that are in distributed environment is common task.
System should ensure the security and authentication. There are number of cryptographic
Techniques. MAC algorithm is used for authentication. Here we have proposed a method for
generation of secured key for MAC algorithm using Novel approach for finger based cryptography
system. The key has been generated using fingerprint patterns, which is stable through out person’s
lifetime. Password can be hacked by trial and error basis. But it is not possible to break the
biometrics based security system.
REFERENCES
[1]. William Stallings, “Cryptography and Network Security Principles and practice”, 2nd Edition,Prentice
Hall,
[2].Umut Uludag, Sharath Pankanti, Salil Prabhakar, Anil K .Jain “Biometric Cryptosystems Issues and
Challenges” Proceedings of the IEEE 2004.
[3]. N.Lalithamani, K.P.Soman “Irrevocable Cryptographic Key Generation from Cancelable Fingerprint
Templates: An Enhanced and Effective Scheme”. European Journal of Scientific Research ISSN 1450-216X
Vol.31 No.3 (2009), pp.372-387
[4] Jain, A.K.; Prabhakar, S.; Hong, L.; Pankanti, S., "Filter bank-based fingerprint matching”, IEEE
Transactions on Image Processing, vol. 9, no. 5, pp: 846-859, May 2000, Doi:10.1109/83.841531.
[5] P.Arul, Dr.A.Shanmugam “Generate a Key for AES Using Biometric For VOIP Network Security”
Journal of Theoretical and Applied Information Technology 2009.107-112.
Authors
Dr.R.Seshadri was born in Andhra Pradesh, India, in 1959. He received his
B.Tech degree from Nagarjuna University in 1981. He completed his Masters
degree in Control System Engineering from PSG College of Technology,
Coimbatore in 1984. He was awarded with PhD from Sri Venkateswara
University, Tirupati in 1998. He is currently Director, Computer Center,
S.V.University,Tirupati, India. He published number of papers in national and
international journals.
Mr.T.RaghuTrivedi received MCA degree from Andhra University, Vizag He
received his M.Tech in Computer Science from Nagarjuna University.He is a
research scholor in S.V.University,Tirupathi, Andhra Pradesh.

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