Personal Privacy Concerns of Facial Recognition Technology Report

This report delves into the critical issue of personal privacy in the context of facial recognition technology, particularly concerning its application in smartphones. It begins with an overview of facial recognition technology, explaining its function and use of biometric data. The report then identifies current privacy risks associated with the use of facial recognition applications, such as Face ID, including potential vulnerabilities and biases. It also projects future risks, like data spoofing and hacking, and suggests steps individuals can take to protect their privacy. Furthermore, the report outlines privacy principles that should be adhered to by facial recognition technology manufacturers, with specific recommendations for Aotearoa New Zealand, focusing on data security, transparency, and privacy by design. The report emphasizes the need for comprehensive data security measures and user awareness to mitigate risks and ensure responsible use of this technology.

Running Head: PERSONAL PRIVACY FROM FACIAL RECOGNITION TECHNOLOGY 1
Personal Privacy from Facial Recognition Technology
Student’s Name
Institutional Affiliation

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Executive Summary
This paper is a report regarding personal privacy security issues related to facial recognition
technology in smartphones. This paper provides an over of facial recognition technology, current
privacy associated with the use of facial recognition applications, and future risks that face this
technology. Additionally, the paper highlights the privacy principles of facial recognition
technology, and lastly recommends facial recognition manufacturers in Aotearoa, New Zealand.
Keywords: Facial recognition technology, privacy, security, and mobile phone

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Table of Contents
Introduction.................................................................................................................................................4
Overview.................................................................................................................................................4
Findings and Discussion..............................................................................................................................5
Current privacy risk of using facial recognition technology on mobile phones.......................................5
Future risks of facial recognition technology...............................................................................................7
Steps that individuals can take to protect their privacy............................................................................7
Privacy Principles for Facial Recognition Technology................................................................................8
Recommendations.......................................................................................................................................9
References.................................................................................................................................................11

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Introduction
Overview
Facial recognition is a high-tech that is used to recognize a human face. This technology
uses a facial recognition system with the help of biometrics to map facial features from a photo
or a video. The system then compares the captured information with a database of known faces
in search to find a match. The facial recognition software utilizes deep learning algorithms to
make comparisons between a live capture or a digital image to the stored faceprint to verify a
person’s identity. The facial recognition system works by identifying the nodal points on a
human face (Mann & Smith, 2017). These nodal points are the endpoints that are used to analyze
the variables of a person's face, for instance, the width or length of the nose, the shape of the
cheekbones, and the depth of the eye sockets. Therefore, this system works by capturing nodal
points data on a digital image of a person’s face and store the data results as a faceprint in a
database (Hadar et al., 2018). The faceprint is thus used as a comparison base with the data
captured from the faces in a video or an image. The facial recognition technology is indeed
useful in multiple applications ranging from security to advertisements, but security and privacy
concerns remain a significant challenge.
Even though facial recognition technology is not a new thing since it dates back to the
1960s, it has not indeed come into its own until recently when it became the mainstream of
mobile phones. With regard to that, this paper prepares a report in regard to how the Aotearoa
New Zealand government will do to ensure the facial recognition technology is not abused and
does not breach an individual's privacy legal rights.

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Figure 1: Star Link Communication, Pvt. Ltd. (2019). Biometrics Face Recognition [Image].
Retrieved from https://www.starlinkindia.com/blog/biometrics-face-recognition/
Findings and Discussion
Current privacy risk of using facial recognition technology on mobile phones
Regardless of how the use of Face ID appears effortless for unlocking phones as
compared to the use of fingerprints, it raises a number of security questions. Studies have found
that in the past, face recognition high-tech has been easily spoofed through simple tricks. For
instance, in 2009, security investigators established that it was possible to spoof a face-based
login system of a laptop by just using a published photo held in front of the camera (Hung, Wu,
Wen, & Chen, 2018). Similarly, in 2011, Android used to have a face unlock feature, which
required an individual to blink in fro of the camera before the phone could unlock itself (Naker
& Greenbaum, 2017). Nevertheless, researchers successfully managed to bypass the security
feature with little Photoshop effects tricks. As a result, these incidents indicate that the use of

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facial recognition technology on mobile phones will experience a lot of privacy risks. Some of
these risks include:
Face ID will make it easier for police to unlock people’s phones: Similar to Touch ID,
Face ID raises questions regarding forced unlocking. When police detain an individual or
kidnapped by criminals, they cannot be in the position to guess the individual's password.
However, they will compel the person to unlock the phone by holding the phone upon the
individual's face until it passes the Face ID scan. Indeed, this is a major privacy issue, but most
of the users cannot think about it until it is too late. Therefore, this technology raises real-time
questions regarding how the system holds up under threat.
The problem of racial bias: For a very long time in history, facial recognition systems
have experienced racial bias, which has been mostly attributed to the absence of diversity in the
databases. In the past, the algorithms used for matching faces have not been good at recognizing
the faces of Chinese, black, and Indian individuals as compared to the white faces, which has
translated into higher error rates (Stark, 2018). While the commercial facial recognition sector
has been able to deal with this challenge by integrating diverse datasets to address the issue, the
question is if the mobile phones industry has undertaken the necessary steps to address this issue.
Possible to extract the data from the phone chip without interfering with the stored data:
The Face ID data will be stored on the phone storage chip because it is challenging to extract
face data. Nonetheless, with the advancements in research in the future, it could be possible to
extract the data from the phone chip without interfering with the stored data. Consequently, this
could place users' data at risk of being exposed to hackers hence the possibility of an individual's
face leaking and being stolen. Since the Touch ID data is solely stored on the phone, facial
recognition data could also be stowed on the phone. The data kept on the phone is typically

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stored on a resistant circuit-level that cannot be easily analyzed, but researchers have started
making progress in breaking some of those protections (Leong, 2019). As a result, this is an
indication that an individual's phone can be stolen that then broke into to interfere with the data
stored on a phone chip.
Future risks of facial recognition technology
The facial recognition data could be spoofed and hacked. Statistics estimate that by
2022, the number of smartphones with facial recognition feature solutions will be more than one
billion. As a result of the increase in these types of mobile phones, they will become ubiquitous,
and with continuous research about the technology, it will be exposed to vulnerabilities.
According to an investigation undertaken by the University of Toronto, who used adversarial
learning to beat a neutral net using another neural net (Biotti & Cook, 2018). This study indicates
that by only adjusting several pixels at the corner of an individual's mouth or eye, they could not
be recognized by the facial recognition technology.
Steps that individuals can take to protect their privacy
When using phones and applications with facial recognition, it is the responsibility of the
user to exclusive use their gadgets by not sharing it with other people. Through this, their
faceprint will be the only image stored in the phone's database. However, sharing the system
with other people could enable the storage of other people's faces in the phone; hence, in case of
theft, their data can be accessed for malicious gains.

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Figure 2: Smith, R. (2017). iPhone 8 FaceID: How does facial recognition work? What are the
risks?. Daily Express. Retrieved from
https://www.express.co.uk/life-style/science-technology/853140/iPhone-8-FaceID-apple-iphone-
x-facial-recognition-risks-safe-how-work
Privacy Principles for Facial Recognition Technology
With the user-facing application of facial recognition technology consistently evolving,
Aotearoa New Zealand should strive to ensure that it has principles that will apply personally
identifiable information (Madhu, Li, & Kamerman, 2015). These principles will be designed to
ensure responsible data usage by corporate businesses that use facial recognition technology in
commercial settings, create a basis of protection for personal data.

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Recommendations
Since facial recognition technology faces a number of privacy and security related issues,
Aotearoa New Zealand government needs to consider the following recommendations:
1. Data security: Aotearoa New Zealand government needs to ensure that facial recognition
mobile developing corporations observe a comprehensive data security, which is
reasonably designed to protect the privacy, integrity, security, and confidentiality of
personal information against risks such as unintended disclosure and unauthorized access
(Romanou, 2018). Therefore, the Aotearoa New Zealand government should develop
security procedures and technologies which are appropriately fit the scope and scale of
the facial recognition data collected and maintained.
2. Transparency: Aotearoa New Zealand government should ensure that manufacturers of
facial recognition offer users with meaningful notice in regards to how the facial
recognition application templates are created and the way this data can be used, shared,
stored, and maintained. Through corporation implementing facial recognition systems
developing and publishing privacy policies that describe the use of their facial
recognition systems in detailed and explicit terms (Daly, 2017). These privacy policies
and educational help centers are used to ensure users understand policies for facial
recognition data.
3. Privacy by design: Aotearoa New Zealand government should work to ensure that facial
recognition manufacturers implement technological controls that support compliance
with the privacy principles, administrative and legal measures. The facial recognition
manufacturers should implement user privacy as well as data security throughout the

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organization by actively integrating security and privacy into facial recognition services
and products at each product development and deployment stage.

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References
Biotti, F., & Cook, R. (2018). Impaired perception of facial emotion in developmental
prosopagnosia: A reply to Van den Stock’s commentary. Cortex, 101, 298-299.
Daly, A. (2017). Privacy in automation: An appraisal of the emerging Australian
approach. Computer law & security review, 33(6), 836-846.
Hadar, I., Hasson, T., Ayalon, O., Toch, E., Birnhack, M., Sherman, S., & Balissa, A. (2018).
Privacy by designers: software developers’ privacy mindset. Empirical Software
Engineering, 23(1), 259-289.
Hung, K. M., Wu, J. A., Wen, C. H., & Chen, L. M. (2018, November). A System for Disguised
Face Recognition with Convolution Neural Networks. In Proceedings of the 2018
International Conference on Digital Medicine and Image Processing (pp. 65-69). ACM.
Leong, B. (2019). Facial recognition and the future of privacy: I always feel like… somebody’s
watching me. Bulletin of the Atomic Scientists, 75(3), 109-115.
Madhu, S., Li, X., & Kamerman, J. (2015). U.S. Patent No. 9,147,117. Washington, DC: U.S.
Patent and Trademark Office.
Mann, M., & Smith, M. (2017). Automated facial recognition technology: Recent developments
and approaches to oversight. UNSWLJ, 40, 121.
https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/
swales40&section=10
Naker, S., & Greenbaum, D. (2017). Now you see me: Now you still do: Facial recognition
technology and the growing lack of privacy. BUJ Sci. & Tech. L., 23, 88.

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Romanou, A. (2018). The necessity of the implementation of Privacy by Design in sectors where
data protection concerns arise. Computer law & security review, 34(1), 99-110.
Stark, L. (2018). Facial recognition, emotion and race in animated social media. First
Monday, 23(9). http://journals.uic.edu/ojs/index.php/fm/article/view/9406