Automated Facial Recognition Authentication (AFRA) System Analysis

This report delves into the Automated Facial Recognition Authentication (AFRA) system, examining its benefits, such as enhanced accuracy, security, convenience, and automation, while also acknowledging potential issues like data storage, image quality, and surveillance angle concerns. The report explores the individual implications for privacy, discussing geometric and photometric approaches, and highlighting how AFRA can be used for user authentication and verification in government services. Furthermore, it analyzes the ethical implications of AFRA usage, particularly regarding the identification of criminals and the authentication of individuals for services like licenses, while also addressing ethical considerations like consent and awareness of facial data collection. The report concludes by discussing the privacy implications, emphasizing the importance of data security, privacy rules, and regulations, and the potential impact on individual banking details and personal information.

Running head: INFORMATION ACCESS AND ITS PRIVACY
INFORMATION ACCESS AND ITS PRIVACY
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1INFORMATION ACCESS AND ITS PRIVACY
Table of Contents
1.0 Introduction..........................................................................................................................2
2.0 Benefits for using AFRA.....................................................................................................2
3.0 Issues that might be faced while using AFRA.....................................................................3
4.0 Individual implications for privacy......................................................................................4
5.0 Ethical Implication of policy usage regarding AFRA..........................................................5
6.0 Privacy Implications of policy usage regarding AFRA.......................................................7
7.0 Will AFRA enhance personal security & privacy................................................................8
8.0 Conclusions..........................................................................................................................9
9.0 References..........................................................................................................................10

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1.0 Introduction
Automated Facial Recognition System is depicted to be an effective and innovated
technology that is capable for identifying as well as verifying an individual with the help of
the digital image from a trusted video source. The facial recognition systems work in multiple
methodologies. However, in general this technology works by making a comparison with the
selected features regarding a single face with the stored various images within the database.
This methodology is also stated as an Artificial Intelligence implied Biometric authentication
which is structured on the basis of an application that tends to identify a person uniquely by
making a critical analysis of the patterns that is necessary to identify a person (Mitra, Wen &
Gofman, 2016). These patterns may be depicted as the individual’s facial shape as well as
textures. The concept of automated face recognition is eventually used in various applications
that varies from different social media platforms as well as advanced authentication systems
such as banking transactions (Zimmerman, 2017). This report will deal with the benefits as
well as issues that the individuals may come across regarding the usage of the AFRA for the
identification of the persons. Moreover, the individual privacy implications in regards to the
ethical policies for the usage of AFRA will also be discussed in the further parts of the report.
2.0 Benefits for using AFRA
The prime benefits that are identified in regards to the usage of the automated facial
recognition authentication are depicted as follows:
Greater Accuracy: The 3D mapping and the deep learning as well as the other advances
makes AFRA much more reliable to use as well as harder to get trick. This technology also
tends to provide accurate results in regards to the facial authentication procedure for certain
applications as well as social media.

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Better security: Several researches have depicted that there exists a single chance within a
maximum of 50,000 chances regarding a phone authentication system to get unlocked with
the wrong identification of faces (Ramachandra & Busch, 2017). However, with the usage of
the 3D modelling architecture of the facial recognition the chances are shifted to 1 in
1,000,000. Thus it is said to enhance the security of the devices in a much more advanced and
effective way.
Convenient: The facial recognition authentication procedure is said to be much more
convenient in regards to the other authentication or security procedures present or used by the
individuals within their systems (Dasgupta, Roy & Nag, 2017).
Smarter Integration: The tools that are used in regards to the integration of the authentic
systems are depicted as the existing security infrastructures which tends to save time as well
as cost in regards to the software development (Prasanna & Reddy, 2017). The authentication
procedures of AFRA will tend to provide smarter integration to the overall security feature of
the stated environment.
Automation: The overall automated system in regards to AFRA is stated to be very accurate
in accordance to the security measures that are required to visually monitor as well as
perform the security checks thus preventing the unauthorized entry of within the system
(Mazlan, Harun & Suliman, 2017).
3.0 Issues that might be faced while using AFRA
There are significantly identified benefits regarding the usage of AFRA but there are
certain issues also (Mann & Smith, 2017). These issues might be faced in regards to the usage
of the automated facial recognition systems. The discussion in regards to some of the issues
are stated below:

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Storing and processing: The storage of the digital images within a database is depicted as the
most significant part in regards to the digital world. The huge amount of data in regards to the
storage that will be used for the future recognition of the faces (Dering & Tucker, 2017). The
big organizations such as in this case the government is trying to imply the technology in
their environment for the facial recognition of the individuals thus providing access to the
related facilities. This storage as well as processing of data needs much more space, which
can be very small in regards to the future work. This might lead to the non-storage of many
data as well as delete the previously stored data within the database.
Image quality and Size: The facial recognition is depicted as the advanced software model in
this modern era, which requires the high quality camera regarding the development of the
algorithm that will tend to operate accurately (Robertson, Kramer & Burton, 2017). The
quality of the image that is to be captured by the cameras must be effectively stored. The
effects of the overall recognition procedure is depicted as a process that needs to be followed
for the future detection of the faces.
Surveillance angle: The process of identification also depends on the fact of the camera
angle that is used for capturing the face of the target. For the enrolment of the faces within
this system, this angle is very necessary in regards to the camera as well as the target (El
Khiyari & Wechsler, 2017). The trouble with respect to this angle may cause a sever failure
of the storage of the faces that will be used for the future recognition.
4.0 Individual implications for privacy
In accordance to the privacy as well as safety of data, the individual implication may
be sated that the usage of AFRA will be significantly important for the protection of the
individual data. Moreover, the traditional approaches that may be taken into consideration in
respect to the usage of this methodology for the protection of data. These approaches are of

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two types one being the Geometric and the other one being the Photometric (Ometov et al.,
2018). The Geometric approach is associated with the focus regarding the distinguished
features of the identities, whereas the Photometric approach deals with the resorting of the
statistical approach. This approach signifies the breaking down of the image into certain
values those of which are compared with the existing values that are already present in the
templates for the elimination of certain discrepancies. This discrepancies may lead to the
mismatch of the faces that are necessary for the facial recognition of the persons those tend to
achieve the data or the personal information stored in a certain database. Moreover, the
technology of AFRA also helps the individuals as well as the authorities to discretely safe
keep the information within the database of the respected organizations. In this case, a
government authority is trying to implement this technology for the user authentication as
well as verification of the users in regards to the various state level activities
(Harakannanavar, Renukamurthy & Raja, 2019). These protocols or the data of the users will
be used by the respective government authorities or the departments for the verification or
legalised procedures within the environment of the government. This storage of the public
information tends to authenticate the criminals too in respect to the facial detection systems
that are used by the government to keep a record of accomplishment of the overall population
of the state in a single database.
5.0 Ethical Implication of policy usage regarding AFRA
The ethical consideration or the implications that are effectively enhanced in regards
to the usage of this technology may be considered as the significant importance by the
government of any country. This will help the government to enhance the identification
procedure of the government authorities as well as officials to significantly identify the
persons that are trying to avail certain facilities from the government (Vazquez-Fernandez &
Gonzalez-Jimenez, 2016). This will include the ethical consideration such as providing a

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driving license or firearm license as well as several considerations that are significantly
identified to be associated within the bounds of the state government. The most important
ethical consideration that is believed to be achieved by the state government is stated to be
the fact of the identification of the criminals that are recorded by the state governments. This
is a very significant protocol that is adhered by the state authorities, as this will help the
procedure to be ease while identifying persons that has done some sort of crime. Moreover,
this will also be significant in regards to the society that is present within the bounds of the
state government that are trying to implement the technology as it may solve the case by the
quick as well as easy identification of the criminals with the help of their faces that will
already be stored in the state government’s database (Tanwar et al., 2019). However, there
are some ethical considerations in some sectors where some persons of the society are trying
to avail the facilities as provided by the state government. This will also help the procedure of
availing the licenses to the persons who are applying for the licenses of driving, firearm,
shops as well as trade and many more. The storage of the information of the individuals will
help the authentication as well as the verification procedure to be quick as well as easy.
The ethical implications regarding the usage of the AFRA is significantly identified at
the facial recognition systems usage at different areas. For an instance let us take an example
of an airport where there are different facial recognition mechanisms used for carrying out the
authentication procedure of the individuals entering as well as exiting at an airport.
Moreover, the passengers or the persons within the airport are not aware of the fact that they
are being recorded by the facial recognition system. This is depicted as one of the significant
violation of the ethical properties of the individuals that are not aware of the fact that their
face are being recorded in a database. Moreover, the facial data as recorded within the
database and the individual is unaware of the fact this also puts a significant negative
approach towards the data privacy of the individuals within the airport (Das et al., 2018). This

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may be implemented for recording most of the individual’s facial data for maintaining an
enhanced database but the consent of the individual is also mandatory as well as necessary.
This will also tend to put an enhanced level of cooperation from the individuals as well as
help the authorities to collect accurate data of the individuals within the database. However,
the ethical implication also lies in the fact that the government must have the consent of the
citizens regarding an implementation of a new technology, which will further ease the overall
working of the government authorities.
6.0 Privacy Implications of policy usage regarding AFRA
The privacy implication in regards to the usage of the AFRA by the government
authorities will firstly help the state government to keep the personal data of the overall
individuals. This will also imply the privacy rules as well as the regulations that are to be
adhered by the overall organizations as well as the authorities within the bounds of the state
government top securely perform the operations that are needed for the safety of the
individuals (Kalyani, 2017). This technology tends to contain the personal information of the
individuals that are present within the state as well as it tends to ease the authorities for the
quick relevancy of the individuals in which service they are availing. Moreover, this also tend
to contain the banking details of the users that will significantly store by the technology and
only after authentication may help the banking authorities to successfully perform any sort of
transaction within the state bounds (Ring, 2016). This will also hold the information of the
individuals that has previous crime records thus helping the police of the state government to
quickly identify the persons involved with any sort of crime that might have triggered within
the state bounds.
The privacy implication of the implementation of the AFRA will also depict some
issues as in case of any accident if the face of the individual that met with the accident is

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purely damaged then it will be difficult to identify using this system. However, in most of the
chances it is depicted that the technology is useful as well as helpful to both the users that are
availing the services as well as those are facilitated by it (Mishra et al., 2015). This will also
provide the state government to save their time in terms of the identification and the
verification procedure. However, enrolling all the details of the individuals within a state is
also difficult for the state authority by the time that will be consumed will be done for a
single time. After that, the facilities will only be followed up by the state government.
7.0 Will AFRA enhance personal security & privacy
The usage of the AFRA will significantly help the state government as well as the
citizens of the state to carry out the legalised as well as effective procedure to be less time
consuming as well as easy than the manual verification as well as authentication procedure.
The one time storage of the information regarding the individuals will help the state
government to significantly identify the persons regarding the facilities that are being tried to
be availed by the users of the services (Sagar & Narasimha, 2019). Moreover, the great
implementation of the technology for the identification of the criminals will also help the
police of the state government to efficiently identify the persons that are involved with the
crime as well as the persons that are planning the crime (Tussy, 2018). The technology of
AFRA will also lead the state to pursue with the digitalised information of the individuals
residing in the state. This will help the individuals to perform the banking transactions safely
with the help of the face recognition mechanism provided by the technology. However, there
is a risk associated with the technology is that since every information is digitalised it will be
very easy for the hackers to make some changes within the database of the stored
information. This must be adhered by the state government to put extra privacy coverage to
the data that is being stored in the database with the help of firewall security as well as

9INFORMATION ACCESS AND ITS PRIVACY
antimalware that will prevent unauthorized access to the data stored in the database of the
state government.
8.0 Conclusions
Thus from the above report it may be depicted that the usage of the technology will
put a great positive impact in the overall technological functionalities of the state
government. Moreover, the facts described within this report is stated to be very useful in
regards to very useful for the implication or the considerations by the state government or the
individuals. The significant usage of this technology by the police department of the stated
department is also depicted to be very useful in terms of the criminal identification within the
bounds of the state. Thus, it may be concluded that the implementation of the technology will
bring a new trend within the state bounds that are trying to implement this technology. This
technology is depicted to be very useful in regards to the storage as well as carrying out the
verification procedure of the individuals that are trying to avail the services that are provided
by the state government. Lastly, it can be said that this methodology will be very helpful if
implemented with proper security as well proper data collection methods to effectively
achieve great results.

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9.0 References
Das, A., Sengupta, A., Saqib, M., Pal, U., & Blumenstein, M. (2018, July). More Realistic
and Efficient Face-Based Mobile Authentication using CNNs. In 2018 International
Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
Dasgupta, D., Roy, A., & Nag, A. (2017). Advances in User Authentication. Springer
International Publishing.
Dering, M. L., & Tucker, C. S. (2017, October). Implications of Generative Models in
Government. In 2017 AAAI Fall Symposium Series.
El Khiyari, H., & Wechsler, H. (2017). Age invariant face recognition using convolutional
neural networks and set distances. Journal of Information Security, 8(03), 174.
Harakannanavar, S. S., Renukamurthy, P. C., & Raja, K. B. (2019). Comprehensive Study of
Biometric Authentication Systems, Challenges and Future Trends. International
Journal of Advanced Networking and Applications, 10(4), 3958-3968.
Kalyani, C. H. (2017). Various biometric authentication techniques: a review. J Biom Biostat,
8(5), 1-5.
Mann, M., & Smith, M. (2017). Automated facial recognition technology: Recent
developments and approaches to oversight. UNSWLJ, 40, 121.
Mazlan, F., Harun, A., & Suliman, S. (2017). Facial Recognition in Multimodal Biometrics
System for Finger Disabled Applicants. Indonesian Journal of Electrical Engineering
and Computer Science, 6(3), 638-645.
Mishra, B., Fernandes, S. L., Abhishek, K., Alva, A., Shetty, C., Ajila, C. V., ... & Shetty, P.
(2015, February). Facial expression recognition using feature based techniques and

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model based techniques: a survey. In 2015 2nd International Conference on
Electronics and Communication Systems (ICECS) (pp. 589-594). IEEE.
Mitra, S., Wen, B., & Gofman, M. (2016). ◾ Overview of Biometric Authentication. In
Biometrics in a Data Driven World (pp. 27-62). Chapman and Hall/CRC.
Ometov, A., Bezzateev, S., Mäkitalo, N., Andreev, S., Mikkonen, T., & Koucheryavy, Y.
(2018). Multi-factor authentication: A survey. Cryptography, 2(1), 1.
Prasanna, D. M., & Reddy, C. G. (2017). Development of Real Time Face Recognition
System Using OpenCV. Development, 4(12).
Ramachandra, R., & Busch, C. (2017). Presentation attack detection methods for face
recognition systems: A comprehensive survey. ACM Computing Surveys (CSUR),
50(1), 8.
Ring, T. (2016). Privacy in peril: is facial recognition going too far too fast?. Biometric
Technology Today, 2016(7-8), 7-11.
Robertson, D. J., Kramer, R. S., & Burton, A. M. (2017). Fraudulent ID using face morphs:
Experiments on human and automatic recognition. PloS one, 12(3), e0173319.
Sagar, D., & Narasimha, M. K. (2019). Development and Simulation Analysis of a Robust
Face Recognition Based Smart Locking System. In Innovations in Electronics and
Communication Engineering (pp. 3-14). Springer, Singapore.
Tanwar, S., Tyagi, S., Kumar, N., & Obaidat, M. S. (2019). Ethical, Legal, and Social
Implications of Biometric Technologies. In Biometric-Based Physical and
Cybersecurity Systems (pp. 535-569). Springer, Cham.
Tussy, K. A. (2018). U.S. Patent No. 9,953,149. Washington, DC: U.S. Patent and Trademark
Office.

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Vazquez-Fernandez, E., & Gonzalez-Jimenez, D. (2016). Face recognition for authentication
on mobile devices. Image and Vision Computing, 55, 31-33.
Zimmerman, H. (2017). The Data of You: Regulating Private Industry's Collection of
Biometric Information. U. Kan. L. Rev., 66, 637.