356 Part II • Predictive Analytics/Machine Learning Face recognition, although seemingly similar to image recognition, is a much more complicated undertaking. The goal of face recognition is to identify the individ- ual as opposed to the class it belongs to (human), and this identification task needs to be performed on a nonstatic (i.e., moving person) 3D environment. Face recognition has been an active research field in AI for many decades with limited success until recently. Thanks to the new generation of algorithms (i.e., deep learning) coupled with large data sets and computa- tional power, face recognition technology is starting to make a significant impact on real-world applications. From security to marketing, face recognition and the variety of applications/use cases of this technology are increasing at an astounding pace. Some of the premier examples of face recogni- tion (both in advancements in technology and in the creative use of the technology perspectives) come from China. Today in China, face recognition is a very hot topic both from business development and from application development perspectives. Face recognition has become a fruitful ecosystem with hundreds of start-ups in China. In personal and/or business settings, people in China are widely using and relying on devices whose security is based on automatic recognition of their faces. As perhaps the largest scale practical applica- tion case of deep learning and face recognition in the world today, the Chinese government recently started a project known as “Sharp Eyes” that aims at establishing a nationwide surveillance system based on face recognition. The project plans to integrate security cameras already installed in public places with private cameras on buildings and to utilize AI and deep learning to analyze the videos from those cameras. With millions of cameras and billions of lines of code, China is building a high-tech authori- tarian future. With this system, cameras in some cit- ies can scan train and bus stations as well as airports to identify and catch China’s most wanted suspected criminals. Billboard-size displays can show the faces of jaywalkers and list the names and pictures of peo- ple who do not pay their debts. Facial recognition scanners guard the entrances to housing complexes. An interesting example of this surveillance system is the “shame game” (Mozur, 2018). An intersection south of Changhong Bridge in the city of Xiangyang previously was a nightmare. Cars drove fast, and jaywalkers darted into the street. Then, in the summer of 2017, the police put up cameras linked to facial recognition technology and a big out- door screen. Photos of lawbreakers were displayed alongside their names and government identifica- tion numbers. People were initially excited to see their faces on the screen until propaganda outlets told them that this was a form of punishment. Using this, citizens not only became .

1. 356 Part II • Predictive Analytics/Machine Learning
Face recognition, although seemingly similar to image
recognition, is a much more complicated undertaking.
The goal of face recognition is to identify the individ-
ual as opposed to the class it belongs to (human), and
this identification task needs to be performed on a
nonstatic (i.e., moving person) 3D environment. Face
recognition has been an active research field in AI
for many decades with limited success until recently.
Thanks to the new generation of algorithms (i.e., deep
learning) coupled with large data sets and computa-
tional power, face recognition technology is starting to
make a significant impact on real-world applications.
From security to marketing, face recognition and the
variety of applications/use cases of this technology
are increasing at an astounding pace.
Some of the premier examples of face recogni-
tion (both in advancements in technology and in the
creative use of the technology perspectives) come
from China. Today in China, face recognition is a
very hot topic both from business development and
from application development perspectives. Face
recognition has become a fruitful ecosystem with
hundreds of start-ups in China. In personal and/or
business settings, people in China are widely using
and relying on devices whose security is based on
automatic recognition of their faces.
As perhaps the largest scale practical applica-
tion case of deep learning and face recognition in

2. the world today, the Chinese government recently
started a project known as “Sharp Eyes” that aims at
establishing a nationwide surveillance system based
on face recognition. The project plans to integrate
security cameras already installed in public places
with private cameras on buildings and to utilize AI
and deep learning to analyze the videos from those
cameras. With millions of cameras and billions of
lines of code, China is building a high-tech authori-
tarian future. With this system, cameras in some cit-
ies can scan train and bus stations as well as airports
to identify and catch China’s most wanted suspected
criminals. Billboard-size displays can show the faces
of jaywalkers and list the names and pictures of peo-
ple who do not pay their debts. Facial recognition
scanners guard the entrances to housing complexes.
An interesting example of this surveillance
system is the “shame game” (Mozur, 2018). An
intersection south of Changhong Bridge in the city of
Xiangyang previously was a nightmare. Cars drove
fast, and jaywalkers darted into the street. Then,
in the summer of 2017, the police put up cameras
linked to facial recognition technology and a big out-
door screen. Photos of lawbreakers were displayed
alongside their names and government identifica-
tion numbers. People were initially excited to see
their faces on the screen until propaganda outlets
told them that this was a form of punishment. Using
this, citizens not only became a subject of this shame
game but also were assigned negative citizenship
points. Conversely, on the positive side, if people are
caught on camera showing good behavior, like pick-
ing up a piece of trash from the road and putting it
into a trash can or helping an elderly person cross an

3. intersection, they get positive citizenship points that
can be used for a variety of small awards.
China already has an estimated 200 million sur-
veillance cameras—four times as many as the United
States. The system is mainly intended to be used for
tracking suspects, spotting suspicious behavior, and
predicting crimes. For instance, to find a criminal, the
image of a suspect can be uploaded to the system,
matching it against millions of faces recognized from
videos of millions of active security cameras across
the country. This can find individuals with a high
degree of similarity. The system also is merged with
a huge database of information on medical records,
travel bookings, online purchases, and even social
media activities of every citizen and can monitor
practically everyone in the country (with 1.4 billion
people), tracking where they are and what they are
doing each moment (Denyer, 2018). Going beyond
narrowly defined security purposes, the govern-
ment expects Sharp Eyes to ultimately assign every
individual in the country a “social credit score” that
specifies to what extent she or he is trustworthy.
While such an unrestricted application of deep
learning (i.e., spying on citizens) is against the privacy
and ethical norms and regulations of many western
countries, including the United States, it is becoming
a common practice in countries with less restrictive
privacy laws and concerns as in China. Even western
countries have begun to plan on employing similar
technologies in limited scales only for security and
Application Case 6.6 From Image Recognition to Face
Recognition

4. Chapter 6 • Deep Learning and Cognitive Computing 357
Text Processing Using Convolutional Networks
In addition to image processing, which was in fact the main
reason for the popularity
and development of convolutional networks, they have been
shown to be useful in some
large-scale text mining tasks as well. Especially since 2013,
when Google published its
word2vec project (Mikolov et al., 2013; Mikolov, Sutskever,
Chen, Corrado, and Dean,
2013), the applications of deep learning for text mining have
increased remarkably.
Word2vec is a two-layer neural network that gets a large text
corpus as the input
and converts each word in the corpus to a numeric vector of any
given size (typically
ranging from 100 to 1,000) with very interesting features.
Although word2vec itself is not
a deep learning algorithm, its outputs (word vectors also known
as word embeddings)
already have been widely used in many deep learning research
and commercial projects
as inputs.
One of the most interesting properties of word vectors created
by the word2vec
algorithm is maintaining the words’ relative associations. For
example, vector operations
vector (‘King’) - vector (‘Man’) + vector (‘Woman’)

5. and
vector (‘London’) - vector (‘England’) + vector (‘France’)
will result in a vector very close to vector (‘Queen’) and vector
(‘Paris’), respectively.
Figure 6.29 shows a simple vector representation of the first
example in a two-dimensional
vector space.
Moreover, the vectors are specified in such a way that those of a
similar context are
placed very close to each other in the n-dimensional vector
space. For instance, in the
word2vec model pretrained by Google using a corpus including
about 100 billion words
(taken from Google News), the closest vectors to the vector
(‘Sweden’) in terms of cosine
distance, as shown in Table 6.2, identify European country
names near the Scandinavian
region, the same region in which Sweden is located.
Additionally, since word2vec takes into account the contexts in
which a word has
been used and the frequency of using it in each context in
guessing the meaning of the
word, it enables us to represent each term with its semantic
context instead of just the
syntactic/symbolic term itself. As a result, word2vec addresses
several word variation
issues that used to be problematic in traditional text mining
activities. In other words,
crime prevention purposes. The FBI’s Next Generation
Identification System, for instance, is a lawful appli-
cation of facial recognition and deep learning that

6. compares images from crime scenes with a national
database of mug shots to identify potential suspects.
Questions for Case 6.6
1. What are the technical challenges in face
recognition?
2. Beyond security and surveillance purposes, where
else do you think face recognition can be used?
3. What are the foreseeable social and cultural
problems with developing and using face recog-
nition technology?
Sources: Mozur, P. (2018, June 8). “Inside China’s Dystopian
Dreams: A.I., Shame and Lots of Cameras.” The New York
Times.
https://www.nytimes.com/2018/07/08/business/china-
surveillance-technology.html; Denyer, S. (2018, January).
“Beijing Bets on Facial Recognition in a Big Drive for Total
Surveillance.” The Washington Post. https://www.washing-
tonpost.com/news/world/wp/2018/01/07/feature/in-
china-facial-recognition-is-sharp-end-of-a-drive-for-total-
surveillance/?noredirect=on&utm_term=.e73091681b31.