This document proposes using fingerprint recognition on mobile devices to secure access to mobile cloud resources. It discusses the existing password-based system and how the proposed fingerprint system offers more security. The fingerprint images are captured using mobile phone cameras, preprocessed, and features are extracted for matching. Experimental results on different phones show processing times within acceptable limits. While this provides more secure authentication than passwords, mobile phone cameras introduce challenges for fingerprint recognition compared to dedicated sensors. The proposed system demonstrates adding security for mobile cloud access within performance standards.

1. Acharya Institute of TechnologyDepartment of ISEDepartment of ISE
Under the Guidance of
Prof. Yogesh N
By
Rahul K N

2. Acharya Institute of TechnologyDepartment of ISE
Contents

3. Acharya Institute of TechnologyDepartment of ISE
Introduction
Mobile cloud computing =
mobile computing + cloud computing
It presents new issues of security threats
such as unauthorized access to resources
exist in mobile cloud.
Here we’re using fingerprint recognition
system to secure mobile cloud.

4. Acharya Institute of TechnologyDepartment of ISE
Difference between Existing
System and Proposed System
Basis Existing System
(Password)
Proposed system
(Fingerprint)
Security Less secure ,as the passwords
can be reused ,phished or key
logged.
More secure, as Fingerprints are
unique and complex enough to
provide a robust template for
authentication.
Identification
Accuracy
Cannot be identified accurately
whether the one who is
accessing the resources is
authorized or not.
Authorized user is identified by his
fingerprint hence there is no
chance of unauthorized user to
get access to the resources in
cloud.
Protection
from the
attacks
Less protected ,as injection
attacks on the database is
possible .
More protected, as injection
attacks on the database is
impossible.
Others Remembering the passwords is
difficult .
Remembering anything is not
required as ultimately all the
system requires is the image of
your Fingertip.

5. Acharya Institute of TechnologyDepartment of ISE
Proposed system
Pre-processing -> convert to gray-scale ,edge
enhancement, filtering, binarization, thinning, map
direction, minutiae extraction
Core point detection
Input Finger image
Feature extraction
The User is Accepted
Matchig?
Data-
base
enrollment
yes
no

6. Acharya Institute of TechnologyDepartment of ISE
EXPERIMENTAL RESULTS AND DISCUSSION
The tests are separated into two parts:
1) Functionality 2)Performance
1. Evaluating Functionality
. Figure 2 summarizes the output from various functions, including (a) the original image, (b)
convert to gray-scale, (c) edge enhancement, (d)filtering, (e) binarization, (f) thinning, (g)
map direction, and (h) minutiae extraction
Figure 2: Functions - Output for Galaxy Note

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Algorithms Used for
Matching
Relative Distance Matching
Image Mapping
Each algorithm having a different
threshold score for matching.

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Simple Algorithm
• The similarity score (S) is the result of the comparison between the
extracted features and features stored in a database.
If (S is low value) then
Little similarity
If (S is high value) then
High similarity
•After that, the decision will be based on the similarity score (S), which is
compared to a predefined threshold (T).
If (S > T) then
The user is accepted
Else if (S < T)
The user is rejected

9. Acharya Institute of TechnologyDepartment of ISE
2. Evaluating Performance
In this section, the process time is calculated for
each function to test if the performance rate is
acceptable according to the rates established by
the National Institute of Standards and
Technology (NIST). Figures 3,4 and 5 show the
process time from testing the fingerprint images for
the Sony Xperia S , Samsung Galaxy S3 and
BlackBerry Z resp.

10. Acharya Institute of TechnologyDepartment of ISE
Figure 3 shows the experiment results of the fingerprint image taken with the Sony Xperia S
device, which recorded 0.9 seconds as the maximum time and 0.4 seconds as the average

11. Acharya Institute of TechnologyDepartment of ISE
Figure 4 illustrates the process time for an image taken with a Samsung Galaxy S3 device the
maximum time is 2.4 seconds and the average is 0.5 seconds.

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Another example is the BlackBerry Z in Figure 5, with approximately 4 seconds as a maximum
time and 0.8 seconds as the average recorded time.

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Discussion
From the experiment results, it is evident that,
 The range for the total processing time to pre-process a fingerprint image takes between 1
and 12 seconds.
Table 1 summarizes the range of process times for each function in the pre-processing class.
An acceptable enrolment time should be equal to or less than two minutes, which means that
the enrolment process must be completed in 120 seconds. When the total process time is
subtracted from the acceptable enrolment time, there are 108 seconds remaining for enrolment.

14. Acharya Institute of TechnologyDepartment of ISE
Figure 6 shows the process time of enrolment for six different mobile devices, and nearly all
are very similar.

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Figure 7 illustrates that 0.2 seconds is the minimum time, 19 seconds is the maximum, and 0.4
seconds is the average. This means that the proposed approach achieved 19 seconds, with 108
seconds being the maximum acceptable time.

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The Matching Process time is shown in Figure 8 for three mobile devices. The average
time is 0.4 seconds, which is an accepted rate.

17. Acharya Institute of TechnologyDepartment of ISE
Features
 The proposed solution is not only to secure
unauthorized access, but also to protect databases
from injection attacks due to the absence of string
input from users. The fingerprint image is the only
input from the user in accordance with the interface
design. No other input is permitted from the user to
enter the system.
 The interface in this solution is based on HTML5,
which is a cross-platform and has been tested on
different mobile platforms.

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Applications
 User doesn’t have to remember the password as the
password would be user’s fingerprint.
As the fingertip’s image is captured by mobile phone’s
camera it is way cheaper than that of getting the fingerprint
by using fingerprint scanner.
It provides more security as compare to the traditional
approaches of authentication.
 With the addition of some filters to segment the method in
this solution, it will be able to work with web cameras.

19. Acharya Institute of TechnologyDepartment of ISE
Drawbacks
Due to the complicated background, hand and
camera shaking during taking photos, the result of
experiments indicates it is impossible to get a
desirable performance of fingerprint recognition
using mobile phone cameras in real-life scenarios
even though it might be working well under
laboratory environment.
mobile phone cameras are mostly optimized to
capture human face or other more “attracting”
objects in a frame instead of fingerprints.

20. Acharya Institute of TechnologyDepartment of ISE
Conclusion
 The focus in this research is on the mobile
cloud and protecting mobile cloud resources from
illegitimate access.
 The proposed solution for authenticating
mobile cloud users using the existing mobile
device camera as a fingerprint sensor to obtain a
fingerprint image, and then process it and
recognize it.
 Results show that the proposed solution has
added value to keep performance at an accepted
level.

21. Acharya Institute of TechnologyDepartment of ISE
References
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• M. O. Derawi, B. Yang, and C. Busch, "Fingerprint Recognition with Embedded Cameras on Mobile
Phones," Security and Privacy in Mobile Information and Communication Systems, pp. 136-147,2012.
• H. T. Dinh, C. Lee, D. Niyato, and P. Wang, "A survey of mobile cloud computing architecture,
applications, and approaches," Wireless Communications and Mobile Computing, 2011.
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• B. Y. Hiew, A. B. J. Teoh, and O. S. Yin, "A secure digital camera based fingerprint verification
system," Journal of Visual Communication and Image Representation, vol. 21, pp. 219-231, 2010.
• B. Hiew, A. B. J. Teoh, and D. C. L. Ngo, "Pre-processing of fingerprint images captured with a
digital camera," in Control, Automation, Robotics and Vision, 2006. ICARCV'06. 9th International
Conference on, 2006, pp. 1-6.

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Any questions ?