Topic
Facial Recognition Systems: Another
Threat to Privacy?
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Instruction
There are at least five sections to this assignment. Your
case study should be in your own words; quote very sparingly. Be concise. This
should be a 5-7 page APA paper
1. SUMMARY:
Summarize the case. Identify the main point (as in “What’s your
point?”), thesis, or conclusion of this case. (5 points)
2. SUPPORT:
Do significant research outside of the book and demonstrate that you have in a
very obvious way. This refers to research beyond article itself. This involves
something about the company/organization/individual or other interesting
related area. Show something you have discovered from your own research. Be
sure this is obvious and adds value beyond what is contained in the case
itself. (10 points)
3. EVALUATION:
Apply the concepts from the appropriate chapter. Hint: The appropriate chapter
is the same number as your case. Be sure to use specific terms and models
directly from the textbook in analyzing this case and include the page in the
citation. (15 points)
4. QUESTIONS:
Address all the case questions. Be sure to answer each question fully. (15
points)
5. SOURCES:
Include citations on the slides and a reference slide with your sources. Use
APA style citations and references. (5 points)
The paper must be in Word and include titles corresponding to
the headings above.
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Case Study : Interactive Session: Organizations Facial
Recognition Systems: Another Threat to Privacy?
Are you on Facebook? Do you worry about how much
Facebook knows about you? Well, as much as it knows now, it’s about to know
much more. Facebook has been investing heavily in artificial intelligence
technology to uniquely identify your face and to track your behavior more
precisely.
Facebook’s facial recognition tool, called DeepFace is
nearly as accurate as the human brain in recognizing a face. DeepFace can
compare two photos and state with 97.25% accuracy whether the photos show the
same face. Humans are able to perform the same task with 97.53% accuracy.
DeepFace was developed by Facebook’s AI research group
in Menlo Park, California and is based on an advanced deep-learning neural
network. Deep learning looks at a large body of data, including human faces,
and tries to develop a high-level abstraction of a human face by looking for
recurring patterns (cheeks, eyebrows, etc). The DeepFace neural network
consists of nine layers of “neurons.” Its learning process has created 120
million connections (synapses) between those neurons using four million photos
of faces.
Once the learning process is complete, every image fed
into the system passes through the synapses in a different way, producing a
unique fingerprint among the layers of neurons. For example, a neuron might ask
if a particular face has a heavy brow. If so, one synapse would be followed, if
not, another path would be taken.
DeepFace soon will be ready for commercial use, most
likely to help Facebook improve the accuracy of its existing facial recognition
capabilities to ensure that every photo of you on Facebook is connected to your
account. (Facebook has one of the largest facial databases in the world for its
photo tagging service.) DeepFace might also be used for real-world facial
tracking, for example monitoring someone’s shopping habits as that person moves
from physical store to store. Facebook could profit handsomely from the
detailed behavioral tracking data collected via DeepFace.
Facebook is one of many organizations using facial recognition
systems, and neural networks are one of several techniques for this purpose.
The Oregon Department of Motor Vehicles (DMV) uses facial recognition software
to ensure that driver’s licenses, instruction permits, and ID cards are not
issued under false names. In Pinellas County, Florida, police can capture a 3-D
video and upload it to an image gallery for comparison to identify people with
prior criminal records or outstanding warrants.
Whatever the technology foundation, facial recognition
systems are raising alarm among privacy advocates, who are worried about the
far-reaching use of people’s facial photos without their knowledge or consent.
Although police departments and DMVs have strict limits on the use of their
facial recognition software, casinos are beginning to faceprint their visitors
to identify high rollers to pamper, and some Japanese grocery stores now use
face-matching to identify shoplifters.
Dr. Joseph J. Atick, one of the pioneers of facial
recognition technology, is at the forefront of these concerns. Atick is in
favor of facial recognition for specific purposes such as law and immigration
enforcement, motor vehicle department authentication, and airport entry, but he
warns about its use for mass surveillance. Atick has been encouraging companies
to adopt policies that safeguard the retention and reuse of facial data,
stipulating that it cannot be matched, shared, or sold without permission.
Another concern is the lack of a legal framework for complying with requests
for facial matching from government agencies.
The impending release of a Google Glass app (glassware)
called NameTag underscores Atick’s concerns about unregulated facial
recognition software. The name, occupation, and public Facebook profile of any
passerby on the street can be obtained by momentarily focusing on his or her
face. Google announced that it would not authorize any facial recognition apps,
but an alternative operating system that bypasses Glass’s swiping and voice
commands allows a picture to be snapped with a wink. A facial recognition app
records the names of people to whom you have been introduced, and a beta
version of NameTag has been released.
Facial analysis has progressed beyond scrutinizing
static features. Frame-by-frame analysis can isolate involuntary
millisecond-long expressions, revealing private sentiments. While these
insights can drive productive endeavors, they are fraught with privacy
implications. For example, do you want the person conducting your job interview
to be able to review a videotape, identify fleeting moments of confusion or
indecision, and decide against hiring you?
Psychologist Paul Eckman studied these fleeting
microexpressions that surface when people are attempting to suppress an emotion
and devised the Facial Action Coding System (FACS). Forty-three facial muscles
control seven primary expressions—happiness, sadness, fear, anger, disgust,
contempt, and surprise. Combinations of other basic muscle movements signal
more advanced emotions such as frustration and confusion. People, and now
computer programs, can be trained to recognize the universal spontaneous
micromovements that divulge peoples’ true feelings—narrowed eyelids, raised
eyebrows, wrinkled forehead, scrunched nose, flared nostrils, or tensed lips.
Large datasets of FACS-catalogued video will soon be
incorporated into computer games. Measuring player reactions to game activity
can, for example, prompt developers to add features or increase game speed at
junctures where players exhibit boredom.
Emotient, another expression analysis start-up located
in San Diego, California, received a $6 million infusion of funds in early 2014
to support glassware for retail salespeople. Customer responses to everyday
exchanges will be measured and evaluated to develop training programs aimed at
optimizing customer service, product offerings, and merchandising techniques.
Emotient is confident that the ability to objectively
and accurately gauge customer emotions will give retail teams more tools to
increase sales, but customer response to being recorded by cameras embedded in
smartglasses is uncertain. The increasingly common tradeoff between an improved
customer selling experience and privacy will have to be astutely navigated,
with the additional burden of gaining customer acceptance for being recorded.
Although facial expression analysis will likely never be
an exact science, academics, business people, and, certainly, government
agencies are intrigued by its possible applications. Online learning could be
improved using Webcams that perceive confusion in a student’s expression, and
trigger additional tutoring sessions. Instantaneous feedback from smartglasses
could help people with autism to navigate a world that is often baffling to
them due to their inability to interpret social cues. And when voice and
gesture analysis and gaze tracking can be combined with facial expression
analysis, the possibilities will explode, along with the privacy implications.
Sources: Sebastian Anthony, “Facebook’s facial
recognition software is now as accurate as the human brain, but what now?”
ExtremeTech, March 19, 2014; Natasha Singer, “Never Forgetting a Face,” New
York Times, May 17, 2014; Ingrid Lunden, “Emotient Raises $6M For Facial
Expression Recognition Tech, Debuts Google Glass Sentiment Analysis App,”
techcrunch.com, March 6, 2014; Anne Eisenberg, “When Algorithms Grow Accustomed
to Your Face,” New York Times, November 30, 2013; and Doug Smith, “Privacy
Concerns over Facial Recognition Software,” myfoxtampabay.com, November 12,
2013.
Case Study
Questions
1.
What are some of the
benefits of using facial recognition technology? Describe some current and
future applications of this technology.
2.
How does facial
recognition technology threaten the protection of individual privacy? Give
several examples.
3.
Would you like DeepFace
to track your activities on Facebook and in the physical world? Why or why not?
