The Evolution of Biometrics and Its Impact on Privacy and Identity

This report examines the evolution of biometric authentication systems, highlighting their increasing importance in protecting identity and privacy. It begins with an abstract that discusses the shift from traditional identification methods to biometric systems, emphasizing the need for enhanced security. The report outlines specific aims, including the development of effective designs for privacy protection using iris recognition and performance comparisons. It also details the objectives, such as improving existing algorithms for better accuracy and creating security systems for iris recognition. The methodology section covers various biometric techniques like fingerprint verification, retinal scanning, and hand geometry, comparing their accuracy and suitability. The report references several sources to support its claims. Overall, this report provides a comprehensive overview of biometric technologies and their impact on identity protection.

Running head: THE EVOLUTION OF BIOMETRICS
THE EVOLUTION OF BIOMETRICS
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1THE EVOLUTION OF BIOMETRICS
Abstract
Biometric authentication is increasing day by day in the last few years to protect the identity
and privacy. The main reason behind this demand is every big company start replacing the
old automatic identification system and implementing a new biometric machine. To identify a
person old system needs a lot of things like a person’s identity number and password. In fact,
the old system is not secure and it fails to protect privacy. The new biometric machine can
identify a person on their specific trait (Patel, Ratha and Chellappa, 2015). Existing system
has many difficulties, one of the major problems are related to the iris recognition and its
performance. Privacy and security are one of the crucial issues in biometric system. This
report is able to determine the aim and objectives of the biometric system which can be
helpful to protect the identity.
Aims:
The aim of this study is to develop an effective design that can protect the privacy and
identity. This design is going to use iris recognition system and it also compares the
performance of the existing system. This study provides the iris pattern security mechanism
that is able to secure the iris recognition system.
Objectives:
The main objectives of this study are provided below:
 To improve the algorithm of an existing system. Old algorithm is not able to identify a
particular person correctly. New algorithm can improve the accuracy as much as
possible on the noisy images.
 A plan of a security system that is able to secure the Irish recognition system.

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 Performance evaluation template by comparing the accuracy of old Iris recognition
system.
Methodology:
Fingerprint verification methodology:
There are different approaches are available for the fingerprint verification. One of
them is traditional matching minutiae, one of them are conventional design matching design
and few of them is able to detect finger (Benaliouche and Touahria, 2014). There are many
types of biometric machines available. Fingerprint biometric provides the best accuracy to
identify a person. Sometimes biometric machines are failed to detect the figure print due to
insufficient disciplined user.
Retinal scanning methodology:
This is the most interesting technology where a device is able to scan the unique
retina pattern via optical coupler. In some cases, retinal scanning provides maximum
accuracy, but the user needs to focus on receptacle (Sadikoglu and Uzelaltinbulat, 2016). This
is not a comfortable option for those people who wear glasses. This the reason many
organization do not use this technology.
Hand geometry methodology:
Hand geometry is able to detect physical appearances of a user’s fingers or hand from
the three dimensional viewpoint. Hand geometry is one of the most acceptable methodology
because it can detect the physical appearances of a particular person (Ahmad et al., 2015).
This is one of the most suitable methodology that can be used in a big organization. The
accuracy of Hand geometry is very good. Organization can modify the accuracy as per
requirements.

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Reference:
Ahmad, I., Jan, Z., Shah, I.A. and Ahmad, J., 2015. Hand recognition using palm and hand
geometry features. Sci. Int, 27(2), pp.1177-1181.
Benaliouche, H. and Touahria, M., 2014. Comparative study of multimodal biometric
recognition by fusion of iris and fingerprint. The Scientific World Journal, 2014.
Patel, V.M., Ratha, N.K. and Chellappa, R., 2015. Cancelable biometrics: A review. IEEE
Signal Processing Magazine, 32(5), pp.54-65.
Sadikoglu, F. and Uzelaltinbulat, S., 2016. Biometric retina identification based on neural
network. Procedia Computer Science, 102, pp.26-33.