Comprehensive Evaluation of Biometrics in Security: Fingerprint & Face

This report provides a comprehensive comparison of fingerprint and face recognition biometric systems. It begins by outlining the strengths and weaknesses of each technology, including the ease of use, accuracy, and potential vulnerabilities. The report then delves into the invasiveness of each technique, addressing privacy concerns and discussing the accuracy levels of both systems. It also explores the methods of collecting biometrics for devices, the deployment strategies of these techniques, and the ease of use for each system. The report concludes by summarizing the findings and highlighting the key differences between fingerprint and face recognition, emphasizing the factors that influence their effectiveness and suitability in various security applications. The report includes references to academic papers and industry resources, providing a well-rounded analysis of biometric security.

Running head: EVALUATING BIOMETRICS IN SECURITY
Application: Evaluating Biometrics in Securit
Name of the Student:
Name of the University:

1EVALUATING BIOMETRICS IN SECURITY
Table of Contents
Introduction................................................................................................................................2
1. Strengths and weaknesses of each system..........................................................................2
2. Invasive are the biometric techniques.................................................................................3
3. Privacy issues considered with each system.......................................................................4
4. Accuracy of each system....................................................................................................4
5. Collecting biometrics for devices.......................................................................................5
6. Deployment of biometric techniques..................................................................................5
7. Evaluation on difficulty to use each system.......................................................................5
Conclusion..................................................................................................................................6
References..................................................................................................................................7

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Introduction
In this paper, the selected biometrics systems are fingerprint identification and face
recognition. Wilson and Hash (2003) discussed that biometrics security is a mechanism
mainly used for authenticating as well as providing access to the system based on automatic
as well as instant verification of the physical characteristics of the individuals. In most of the
organization, biometric verification is required by which the person can identified by
evaluation of biological traits. In this paper, there is discussion of strengths as well as
weaknesses of each biometric system along with privacy issues considered in the system.
There is evaluation of each system functions to verify if the system is safe to use in the
organization.
1. Strengths and weaknesses of each system
Strengths of face recognition system:
i. By means of this biometric system, it is easier to track terrorists, thieves and criminals
with help of face scanning. The hacker cannot hack this technology as there is nothing
to change and steal.
ii. Recognition of face takes second, therefore it is rapid and well-organized verification
of individual.
iii. The facial recognition is easily integrated and there is no need to spend extra money
on integration and other facial solutions (Ammour, Bouden, & Amira-Biad, 2017).
Weaknesses of face recognition system:
i. There is vulnerability of recognition when there is slight change in angle of camera as
well as change in people’s appearances lead to errors in recognition.

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ii. In order to deliver proper results, the machine learning technology is required huge
data sets.
Strengths of fingerprint identification system:
i. It is unique and never be same with other person as each individual person in the
world have different fingerprints.
ii. It requires small storage space to store the databases of the fingerprints.
iii. It is a cost effective based security solution as a small handheld scanner can set up to
take the fingerprints and benefit from high accuracy level (Fu & Yang, 2018).
Weaknesses of fingerprint identification system:
i. There are technical failure in the scanner can lead to error in fingerprint scanning.
ii. Older people with the history of manual work should struggle to register the prints in
system and loss of fingers or hands can lead the person excluded from this biometric
system (Bose & Kabir, 2017).
2. Invasive are the biometric techniques
The face recognition is invasive technology which is mainly used for the surveillance
purposes. This technology is using database of photos for identifying people in security
photos. It is using biometrics for identification of people with its key face features. The most
important feature is geometry of face like distance between eye of person as well as distance
from the forehead to chin (Kundu & Sarker, 2017). It is termed as facial signature where
mathematical formula is used to compare database of identified faces.
The fingerprint tracking is considered as more invasive than always. It is a way to
identify people based on comparison of two of person’s fingerprints. It is a way to identify

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people automatically by its unique fingerprint (Kudu, Student, & Karamchandani, 2016). It is
easier to use and use small space to store the data.
3. Privacy issues considered with each system
The facial recognition system threatens the privacy rights. The main reason of privacy
issue is lack of federal regulations to use of the facial recognition technology. In some places,
this system is failed due to inaccuracy of identifying people due to its color (Devan et al.,
2017). Misidentification of person can lead to wrongful opinions.
In case of fingerprint system, it is possible that the fingerprint scanner of phone is
being hacked. By using inkjet printer and special ink, the hacker can hack the phone and get
the fingerprint.
4. Accuracy of each system
The accuracy of facial recognition system is lower as compared to fingerprint
identification. Accuracy of this system is based on factors like quality of camera, distance,
size of database, gender, subject race and light. It is less accurate when identification of
people of color as well as women (Almudhahka, Nixon, & Hare, 2016). The failure of this
system would cause harm to innocent people by misidentification, prevention of law
enforcement actions and violation of privacy act needs to inform public of changes to the
record system.
The fingerprint identification system is accurate as it is almost not possible to get
same fingerprint of two person. Therefore, it is difficult to get the fingerprint and login in the
system. It is analyzed that this system is 98.6% accurate with single fingerprint test, around
99.6% accurate with two fingerprint testing and 99.9% with four or more fingers (Ali et al.,
2016). Therefore, it is found that it has accuracy rate of 99.9% when considering four or more
fingers of person.

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5. Collecting biometrics for devices
A picture of face is being captured from photo. The image should be looking straight
ahead. The software then reads geometry of the face including face recognition factors like
distance between eye and distance from forehead to the chin, and the result is facial signature.
After that determination is being made and the face print is matched with the image into the
facial recognition database (Ammour, Bouden, & Amira-Biad, 2017). In this way, the facial
recognition biometrics is collected for the device.
An optical scanner is worked by shining a light over the fingerprint and taking a
digital photo of fingers. The image is feeds in computer scanner. The scanner users light
sensitive microchip for producing the digital image (Whitman & Mattord, 2012). There is
analyzing of image on automatic basis, selecting the fingerprint and using the matching
software to turn the image in code. In this way, the fingerprint databases are stored in the
computer scanner.
6. Deployment of biometric techniques
The facial recognition system is worked by extraction of facial features captured on
the digital video images and then compared the data with analyzed faces stored into the
database. Local Binary Pattern Histogram is a face recognition algorithm which represents
local features in images (Almudhahka, Nixon, & Hare, 2016).
The fingerprint system is using digital imaging technology to get, store as well as
verify the data of fingers. The optical sensor is used to take image of fingers and pattern
matching is used to detect the duplicates.

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7. Evaluation on difficulty to use each system
The face recognition system is easier to use as it takes few seconds to identify the face
of the person when standing in front of scanner.
In case of fingerprint identification system, its usability is based on various situations
like where it is used and acceptance by system users. It is less easy to use as compared to face
recognition as in this case it is possible that the user is not familiar with the system and not
know where to put the fingers to scan it (Fu & Yang, 2018). It takes some seconds to identify
the fingerprint, unless the user puts right finger on the scanner. It is also possible that the
fingers are not clear failed to identify it.
Conclusion
It is concluded that as comparison of biometric techniques or systems, fingerprint
recognition is popular in most of the business organizations. The companies are not even
fully trusted face biometric technology and not used it fully as security purpose. It is found
that using face recognition is hassle free as compared to fingerprint as it releases person from
hassle to move thumb and index finger to a specific place to scan it. Fingerprint is considered
as better authentication system as it is 99.9% accurate when used for four or more fingers. It
is a secure way to login any devices until the fingerprint of a person is hacked by third
person.

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References
Ali, M. M., Mahale, V. H., Yannawar, P., & Gaikwad, A. T. (2016, March). Overview of
fingerprint recognition system. In 2016 International Conference on Electrical,
Electronics, and Optimization Techniques (ICEEOT) (pp. 1334-1338). IEEE.
Almudhahka, N., Nixon, M., & Hare, J. (2016, February). Human face identification via
comparative soft biometrics. In 2016 IEEE International Conference on Identity,
Security and Behavior Analysis (ISBA) (pp. 1-6). IEEE.
Ammour, B., Bouden, T., & Amira-Biad, S. (2017, October). Multimodal biometric
identification system based on the face and iris. In 2017 5th International Conference
on Electrical Engineering-Boumerdes (ICEE-B) (pp. 1-6). IEEE.
Bose, P. K., & Kabir, M. J. (2017). Fingerprint: a unique and reliable method for
identification. Journal of Enam Medical College, 7(1), 29-34.
Devan, P. A. M., Venkateshan, M., Vignesh, A., & Karthikraj, S. R. M. (2017). Smart
attendance system using face recognition. Advances in Natural and Applied
Sciences, 11(7), 139-145.
Fu, K., & Yang, Y. (2018, May). Design of Fingerprint Identification System based on
FPGA. In 8th International Conference on Social Network, Communication and
Education (SNCE 2018). Atlantis Press.
Kudu, N., Student, M. E., & Karamchandani, S. (2016, March). Biometric identification
system using fingerprint and knuckle as multimodality features. In 2016 International
Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) (pp.
3279-3284). IEEE.

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Kundu, S., & Sarker, G. (2017). A person identification system with biometrics using
modified RBFN based multiple classifiers. In Proceedings of the First International
Conference on Intelligent Computing and Communication (pp. 415-424). Springer,
Singapore.
Whitman, M., & Mattord, H. (2012). High-assurance computing: Topics & case studies.
Boston, MA: Course Technology/Cengage Learning.
Wilson, M., & Hash, J. (2003). Building an information technology security awareness and
training program (NIST Special Publication 800-50).