Emotion recognition technology White Paper written by Quantum Lab. Describes uses of Emotion Recognition technology in fields of advertisement testing and Customer Experience. Describes methods of validation.
2. We believe that most of
the problems in the world,
business and personal, are
the result of human nature.
We believe that in order to
change something, you have
to be able to measure it.
We believe that in order to
measure something, you have
to understand it.
We believe that emotions
inspire behaviors and inaction.
3. Therefore, our mission is
to enable businesses to
automatically understand
human nature and make
a positive change where it
was not previously possible.
Bartosz Rychlicki,
CEO and Co-Founder Quantum Lab
4. Contents 4
Contents 1. Quantum Lab 5
1.1. Mission and vision 6
1.2. Quantum Lab experts 7
2. Quantum Sense 9
2.1. Technology 10
2.2. Measurement effectiveness accreditation 13
2.3. Quantum Sense vs. Human 15
3. Quantum Insight 17
3.1. The power of emotional advertisement 18
3.2. Quantum Insight 22
3.3. Insight Score 23
3.4. Quantum Insight Applications 24
3.5. Presentation of results 28
3.6. Quantum Insight Functionality 30
4. The final word 31
5. Awards 32
6. Bibliography 33
6. Quantum Lab 6
MISSION AND VISION
The issue of emotions was the subject of discussion for
many prominent thinkers and scientists over the centu-
ries. The golden era of research in emotions fell in the se-
cond half of the last century, along with the work of the
pioneer in the field of mobile-mimic expression of emo-
tions - Paul Ekman. The same period witnessed the deve-
lopment of the explorations of the phenomenon of adver-
tising. The effects of the work of that time gave birth to
the phrase „good publicity is emotional publicity.” In the
year 2013 the fascination and passion for those subjects
resulted in forming the Quantum Lab Company. Company
directors and managers reported a need for support in
issues related to human behavior. Thus our mission was
established. We recognize the problems of business orga-
nizations and provide them with an effective solution thro-
ugh automation, psychological know-how and technology.
We wanted to apply psychological knowledge in practi-
ce in order to allow simple use of its resources. We know
the rules allowing for the analysis of emotions and beha-
vioral responses. We were able to teach this to computers.
We track and measure emotions on the basis of scientific
knowledge from the field of affective computer science
and psychology. The continuous increase in computing po-
wer is opening up new opportunities for us to implement
more complex algorithms and processes.
We don’t just generate data, but also make them
understandable and operable. The basis of this process is
the Quantum Sense technology, designed by us. It enables
the analysis of emotions and behavior of people in real time.
Based on this, we have built two tools - Quantum Insight and
Quantum CX which, by means of a camera, recognize
and classify emotional and behavioral reactions, presen-
ting them in the form of objective data. The first of these
solutions is used primarily in marketing, while Quantum CX
works in the retail area. The foreign branch of the com-
pany is located in the USA. Our solutions are used by va-
rious research institutions and academic circles.
From day to day our technology becomes more and
more „human”, in the context of understanding of what it
„sees.” In the near future we want to make it able to learn
automatically. In this way, we shall implement a kind of
experience in it. We want to be able to obtain information
from multiple channels (e.g. sound), thereby acquiring other
„senses”. Multidimensional, aggregated data would com-
plement the technology, greatly enhancing its capabilities.
7. Quantum Lab 7
QUANTUM LAB EXPERTS
The cardinal advantage of our technology and tools built
based on it is our multidisciplinary and talented team of
R & D Quantum Lab specialists.
KRZYSZTOF CYWIŃSKI
Chief Technology Officer - directs production, is respon-
sible for the development of applications and analyzing
machine data. Deals with the design of neural networks
that recognize and analyze emotions and behavioral in-
dicators, and their implementation in sectors based on
non-verbal communication, emotions and human behavior.
Krzysztof is the most rational part of the team and there
is nothing impossible for him to do.
DARIUSZ KAMOWSKI
Our AI Software Engineer, supports the entire process,
dealing with the analysis of the received data and process
optimization and diagnostics, based on the use of artifi-
cial intelligence. He is an enthusiast, a winner and finalist
of competitions in the areas of electrical power, automa-
tion, nanotechnology and information technology. He is
responsible for implementation of solutions in the field of
computational intelligence and digital signal processing.
8. Quantum Lab 8
JUSTYNA LESZCZ
Research Specialist, psychologist. Author of projects in
the area of non-verbal communication and emotional
intelligence. Her passion is people and statistics.
In Quantum Lab, she takes care of the scientific basis
for projects and is respon-sible for the development
and validation of solutions to the problems of human
nature with psychological methods.
Thanks to close cooperation in the development and im-
plementation of projects, they contribute comprehensive
knowledge and experience in the area of artificial intelli-
gence, psychology and affective computing to the tech-
nologies and products of Quantum Lab, thereby granting
them a prominent place in the world of methods for auto-
matic analysis of human behavior and emotions.
10. Quantum Sense 10
happiness
disgust
anger
sadness
neutral
surprise
TECHNOLOGY
Quantum Sense is a proprietary technology developed
by Quantum Lab, which allows an automatic analysis
of behavioral and emotional indicators occurring on
a human face. By means of micro-expressions detection,
it effectively recognizes five basic emotions: joy, sadness,
surprise, disgust, anger and lack of emotion, called the
neutral state. Using computer vision and artificial intel-
ligence, our technology classifies emotions arising in re-
sponse to a specific stimulus (e.g. video).
Besides the analysis of facial expression, also used
is the analysis of non-verbal factors, such as body
positioning, gestures, posture, general mobility and ove-
rall response time to certain stimuli. Our invention will
soon also be able to verify verbal factors, e.g. acoustic
speech parameters, patterns of speech rate, the con-
tent of speech or non-linguistic sound responses (such as
sighs, shouts or yawns).
11. Quantum Sense 11
need
stimulusemotion
reaction
level of satisfaction
Due to use of this modern technology, the analysis
process of emotion has never been so simple. The test is
non-invasive - it does not require any devices attached to
the human body. To make a measurement, only a standard
camera is required, available in most laptops. The analysis
of facial expressions may be used alone or in combination
with biometric sensors such as EEG, GSR, EMG, FMRI or
using eye tracking devices. The use of Quantum Sense
allows to determine the true opinion about a particular
experience, based on the emotional reaction of customers
and recognized patterns of behavior. The use of measure-
ments instead of declarations eliminates the subconscious
resistance of clients to expressing negative judgments
and allows to avoid cognitive errors.
The process of Quantum Sense operation can be presen-
ted using the following steps:
• Faces and their positions are located in the source ma-
terial, which can be a video recording, a camera image
or a photograph. The search process is supported by
a very thorough and fast algorithm, so even a significant
deviation from the frontal position is acceptable.
• In the next step, the area of the face is normalized - among
other things, the face is scaled, centered, and subjected to
filtration, with particular recognition of lighting compen-
sation and impact of movement. This treatment allows for
a significant correction or complete elimination of the in-
fluence of light, and the correction of the face positioning.
12. Quantum Sense 12
1.
2.
3.
FINDING THE FACE
In the first step, the algorithms find
and define the position of the face.
CLASSIFICATION OF EMOTIONS
Automatic classification of
facial points according to known
expressions of emotion.
EXTRACT CHARACTERISTIC POINTS
Arranged, among others, around the
eyebrows, eyes, nose, mouth.
• A face thus „prepared” is subjected to an algorithm, al-
lowing for separation of its characteristics and physical
properties. An active shape model is the applied to the
face image. This method is able to identify 68 specific
points on the face. This process allows the determination
of how various points behave during facial expressions,
appearing under the influence of emotions.
• Subsequently, the pre-prepared machine learning algori-
thms, based on the use of artificial intelligence, come to
our aid. Thanks to them we are able to translate the above
dependencies to the automatic classification of emo-
tions for the face, which were not visible in the process
of learning classifiers. Quantum Sense uses a variety of
computational intelligence techniques for this purpose,
dominant among which are:
• the support vector machine - SVM,
• the artificial neural network,
• various algorithms from the deep learning family, e.g.
Convolutional Neural Networks - CNN.
Based on Quantum Lab technologies, we have deve-
loped two tools: Quantum CX (Customer Experience) and
Quantum Insight. The first of these is a tool that supports
managers and restaurant owners. It is used to measure the
level of service quality and levels of guest satisfaction. It
is able to identify these two parameters on the basis of
data such as talk time, eye contact, presence in the work-
13. Quantum Sense 13
place. Thanks to our technology managers may, in an easy,
fast and automated way diagnose and subsequently im-
prove, along with the staff, the style of service, which wo-
uld be beneficial to everyone involved. The Quantum Lab
technology also has a number of applications in various
fields and sectors of market and research. In focus gro-
ups, it allows to detect emotional reactions to the physical
product or advertising. It also guarantees objective client
opinion polls’ results (based on subconscious reactions,
not on questions). Our technology may also be used as a
security tool, e.g. at airports, mass events or in vehicles. It
allows for detection of frustration / aggression or tired-
ness of the car or bus driver. Quantum Sense is an effective
tool for the analysis of frustration when using products or
services. It allows real-time matching of services and digital
products to the feelings of users (e.g. a game that changes
its story depending on the reaction of the players). Another
area in which our technology may find a variety of applica-
tions is robotics. Due to copyright solutions proposed by
Quantum Lab, in the future we may be able to grant
robots with empathy skills.
MEASUREMENT EFFECTIVENESS ACCREDITATION
The process of assigning and detection of micro-expres-
sion on the basis of a camera image is a complex pro-
cess. To be able to claim precise obtained results, we have
developed rigorous measurement and data processing
steps. This methodology grants us complete confidence
in the quantitative results obtained and offers the possi-
bility of their qualitative analysis.
QUANTUM SENSE VALIDATION
Cross-validation, otherwise known as a cross-testing,
allows to specify how the classifier trained model will
behave in the event of data that were not used in its con-
struction. In other words, we test the accuracy with which
Quantum Sense determines facial emotions of a particu-
lar face, based on the classifier learned on a database,
created by a team of Quantum Lab specialists.
To obtain a reliable result in the use of cross-valida-
tion, we apply k-fold validation. The original learning set is
divided into K subsets. Then, sequentially, each of them
serves as a test set, and the rest as a training set. Further
analysis is performed. Analysis is thus performed K times.
Then K results are averaged to obtain a single result, defi-
ned in this case as a result of cross-validation. Quantum
Sense obtains up to 95.7% by this method, which puts it
at the forefront of technologies used for automatic ana-
lysis of facial expression.
The cross-validation method is reliable and widely
used in technical and information sciences. Aware of the
importance of delivered results and their accuracy, we
have decided to go a step further than others and deve-
lop a creative tool to verify that the procedures used by
us actually lead to the planned results.
14. Quantum Sense 14
fig. 1. Quantum CX built on Quantum Sense technology currently
used in retail and security.
fig. 2. Quantum Insight built on Quantum Sense is a marketing
tool used by research agencies, media houses and TV
stations.
15. Quantum Sense 15
THE TEST PLATFORM
Along our way to adapt our technology to the highest qu-
ality standards, we have agreed that the cross-validation
procedure in itself is insufficient for the improvement
of algorithms. From this need arose our copyright solu-
tion, allowing to specify the increase in effectiveness of
subsequent versions of the emotions recognition engine –
the test platform.
It enables the analysis of real video recordings by com-
petent judges. With its use, with an accuracy to a single
frame, the emotional reaction of the respondents in the
camera frame is determined by experts. In this way, we
obtain an objective assessment of the subjects’ emotions.
Then, the results from the technology of automatic reco-
gnition of emotions and the determinations made by the
judges are compared. According to the golden rule guiding
our actions - „if you don’t measure something, you cannot
improve it,” we have obtained hard evidence of accuracy
of Quantum Insight in the form of research results.
The development and validation of the double me-
ans of validating the results aroused our curiosity to
check the accuracy in recognition of human emotions by
humans themselves. To have a clear picture of the effec-
tiveness of our technology and the natural human ability,
we designed a study, which clearly defined the efficiency of
Quantum Sense.
QUANTUM SENSE VS. HUMAN
Intrigued by the fact that the measurement of emotional in-
volvement helps to prepare an effective marketing campaign,
we strive for continuous improvement of our technology. To
this end, we have conducted a study comparing the effecti-
veness in detecting facial micro-expressions by our techno-
logy and by the human eye.
We selected two video recordings containing the emo-
tional reactions of people in the frame. These reactions have
been identified and marked by competent judges, using spe-
cialized software. Subsequently, the respondents were pre-
sented with both materials, one after the other. The task of
the study subject was to determine what emotion is expres-
sed by the subject on video. The researcher had to mark, in
real time, second by second, the emotion chosen from the
list by the participant: 1. Anger; 2. Disgust; 3. Joy; 4. Grief;
5. Surprise; 6. Neutral. The same films were analyzed by the
Quantum Sense technology.
THE RESULTS WERE SURPRISING
The average result for all respondents in both films is
46,33% efficiency, with men showing greater accura-
cy in determining emotions than women (the average for
women was 44.96%, while men scored 47.96%). Quantum
Sense recognize, on the other hand, recognized emotions
with the efficiency of 83.28%. Thus, the test results con-
16. Quantum Sense 16
firmed an effectiveness of our technology in the diagno-
sis of human emotions twice better, when compared with
the skills of an average person.
The long-lasting work on designing and validating
Quantum Sense and its satisfactory results, finally allowed
for the commercial application of technology developed by
Quantum Lab. We could hardly wait to grant the possibility
of solving the problems of human nature to anyone for
whom it constituted a daily challenge. The first tool that
our clients could use the benefits of was Quantum Insight.
fig.3. The results of the effectiveness of detection of 5 basic emotions and
the neutral state by humans and the Quantum Sense technology.
vs.
Human
18. Quantum Insight 18
THE POWER OF EMOTIONAL ADVERTISEMENT
The impact of emotions on the daily decisions the average
person is fascinating - they determine the purchase of a ju-
ice to drink, a shampoo or a specific car model, as well as
the choice of a career or a life partner. The phenomenon
of emotions lies in the fact that, as one of the three atti-
tude-forming components, they imply our judgments, and
ultimately translate to actions (McDuff, Kaliouby, Senechal,
Demirdjian & Picard, 2014). Research conducted by Bradley
et al. clearly shows the fact, that the key role in remembe-
ring the presented objects is played by arousal and pleasure
from exposure to the material. In other words, content eli-
citing an emotional response is better stored in memory
and subsequently remembered more easily.
„Emotional campaigns have almost twice the
potential of achieving great financial effects of
the rational ones – even in rational categories”
Binet and Field
The aforementioned principles have been used in mar-
keting. As indicated by the results of Berger and Milkman,
up to 85% of today’s audiences consume video for en-
tertainment, relaxation or excitement. A good video is,
above all, about emotions. Similarly speaking, effective
advertising is based on the appropriate dosage to viewers.
Measuring emotional involvement helps to prepare an ef-
fective marketing campaign. In this process, not only know-
ledge matters, but also the appropriate tools to determi-
ne what emotions the material elicits (Teixeira, Wedel &
Pieters, 2012).
Practice shows that advertising that triggers very
emotional responses from viewers is more effective, in
comparison with materials that are neutral for the reci-
pient. The research by Millward Brown - an international
company engaged in market research and public opinion
-covered an analysis of over 12,000 facial expressions.
Expertise has shown that facial expressions are a relia-
ble predictor of the degree of viewer sympathy for the ad.
Moreover, on this basis, we can predict the intention to
purchase the advertised product.
Emotional processes play a fundamental role in the ad-
vertising message, as the process of perception, memo-
ry and recall depends on them. Emotional ads are more
effective than rational messages – according to Binet and
Field - experts in modern marketing. In „The Long and the
Short of it” in Harvard Business Review, they explain how
19. Quantum Insight 19
How emotions affect the planning and effectiveness of
advertising?
New empirical data shows that emotional advertising model
and measuring the contents emotional response, lead
to greater efficiency, effectiveness, better planning and
decision-making during content design.
Wood, O., Using an emotional model to improve the measurement of advertising
effectiveness. BrainJuicer.
emotional involvement of the viewer enhances loyalty, at
the same time reducing the price sensitivity of the custo-
mers, and thus, bringing twice the profits. In other words,
building an emotional connection with the brand makes the
rational message better reach the potential customers.
It is worth to refer to reflection at this stage, how
much more effective advertising would be, if we were
able to predict what emotions, at which time and in what
combinations should appear to the recipient to create an
emotional connection with the brand, and consequently
buy the presented product and recommend a particular
brand to their friends.
QUANTUM INSIGHT ALREADY KNOWNS ALL THAT
How much rationality and how much emotions?
Emotional ads are much more effective than rational content,
especially in the long perspective. They generate twice as
much profit and can sustain it much longer than a rational
message
Binet, L., Field, P. (2013). The long and the short of it: 10 key principles of success.
Raport Instytutu Praktyków Reklamy (IPA).
FACT 1
FACT 2
20. Quantum Insight 20
THE INCREASED VALUE OF FULLY CONNECTED
CUSTOMERS RELATIVE TO HIGHLY SATISFIED
ONES VARIES BY CATEGORY. HERE ARE THE
VALUES FOR THE NINE CATEGORIES SAMPLED.
HOUSEHOLD
CLEANER
PURCHASES
+103%
TABLET APP
PURCHASES
+82%
CREDIT
CARD
SWLPES
+68%
ONLINE
RETAILER
PURCHASES
+52%
HOTEL ROOM
STAYS
+41%
DISCOUNT
STARE
VISITS
+37%
CONSUM-
ERBANKING
PRODUCTS
+35%
FASTFOOD
VISITS
+27%
CASINO-
GAMING
SPENDING
+23
CUSTOMER VALUE
in relation to highly satisfied
customers
-18%
BASELINE
+13%
+52%
NOT EMO-
TIONAUY
CONNECTED
HIGHLY
SATISFIED
but not fully
connected
PERCEIVE
BRAND
DIFFEREN-
TIATION and
satisfied, but
not fu lly con-
nected
FUUY CON-
NECTED and
satisfied, and
able to per-
ceive brand
differentiati
on
Customers who feel an emotional connection to
the brand are on average 52% more valuable
income-wise compared to customers that are
merely satisfied.
Magids, S., Zorfas, A., Leemon, D. (2015). The New Science of Customer
Emotions. In: Harvard Business Review. Noweber, 2015
THE VALUE OF EMOTIONAL CONNECTION
AS CUSTOMERS’ RELATIONSHIPS WITH A BRAND DEEPENS, THEY MOVE ALONG THE PATHWAY TOWARD
FULL EMOTIONAL CONNECTION. ALTHOUGH THEY BECOME MORE VALUABLE AT EACH STEP, THERE’S A DRA-
MATIC INCREASE AT THE FINAL ANE: ACROSS A SAMPLE OF NINE CATEGORIES, FULLY CONNECTED CUS-
TOMERS ARE 52% MORE VALUABLE, ON AVERAGE, THAN THOSE WHO ARE JUST HIGHLY SATISFIED.
SOURCE SCOTT MAGIDS, ALAN ZORFAS, AND DANIEL LEEMON
FROM “THE NEW SCIENCE OF CUSTOMER EMOTIONS,” NOVEMBER 2015
FACT 3
21. Quantum Insight 21
Should my advertisement evoke emotional reactions?
The Millward Brown study showed that the more emotions
appear in the ad, the better the ad is remembered and the
greater consumer involvement it generates, which in turn
translates into more sales.
Brown, M., (2009). Should My Advertising Stimulate an Emotional Response? Millward
Brown: Knowledge Point.
What makes on-line content viral?
Research shows that the key to effective content virality
is emotional engagement. It turns out that content that
triggers high emotional engagement is more likely to be
shared.
Berger, J., Milkman, K. (2012). What Makes online Content Viral? Journal of Marketing
Research, Vol. 49, No. 2, pp. 192-205
Emotional involvement when watching internet advertising.
Studies have shown that the emotions of surprise and
joy focus consumers’ attention on the ad for longer and
therefore make them stay in front of a computer screen for
longer.
Teixeira, T., Wedel, M., and Pieters, R. (2012). Emotion-induced engagement in internet
video ads. Journal of Marketing Research, Vol. 49, No. 2, pp. 144-159.
The key role of emotional arousal and pleasure in making the
ad stick in consumers’ memory.
It is easier to remember and recall content that evokes
strong emotional arousal.
Bradley, M. M., Greenwald, M. K., Petry, M. C., Lang, P. J. (1992). Remembering pictures:
Pleasure and arousal in memory. Journal of Experimental Psychology: Learning, Memory,
& Cognition 18 (2): 379–390.
The analysis of more than 12,000 facial expressions has
showed that it is possible to use them in order to predict the
extent to which consumers will like the ad and will be willing
to buy the product it advertises.
McDuff, D., El Kalioubi, R., Cohn, J. F., & Picard, R. (Accepted with revisions). Predicting
ad liking and purchase intent: Large-scale analysis of facial responses to ads. IEEE
Transactions on Affective Computing.
FACT 4
FACT 5
FACT 6
FACT 7
FACT 8
22. Quantum Insight 22
QUANTUM INSIGHT
Theexpressionpatternsofsixbasicemotionsareintercultural,
universal. Quantum Insight is able to recognize five of them –
anger, disgust, joy, sadness and surprise and, in addition,
the neutral state. It is based on the Facial Action Coding
System (FACS). This system enables the identification
of facial emotional reactions, appearing in the form of
micro-expressions.
The mimic reaction appears automatically in response
to an outside stimulus. The response time is too short
to register it each time and, consequently, to effectively
recognize emotions. Quantum Insight comes to aid here,
free of such limitations (it uses a camera to “look” at
the world), perfectly capturing every micro-expression
appearing on the face of the observer. Thanks to intelligent
algorithms, Quantum Insight can recognize the emotions
of the audience watching an advertisement spot with an
accuracy of 95.7%.
Courtesy of Quantum Insight, we can measure the
emotional load of a marketing message, as well as provide
real-time data. Such on-line procedure has a number of
advantages - the research group can be made up of respon-
dents from around the world, and the results are available
in as little as 72 hours from the moment of placing an order.
In addition, the natural conditions for making decisions
protect against the influence of disturbing factors on the
reactions of respondents.
The whole survey takes place
in the respondent’s home
In any place in
the world
Measures natural
and spontaneous
emotional reactions
of viewers
Quantum Insight
platform runs within
the cloud
Data is provided in real time, and all
results are available 72 hours after
the test
23. Quantum Insight 23
INSIGHT SCORE
The Insight Score is a proprietary algorithm developed
by Quantum Lab. It defines the emotional structure of
advertising - analyzes the frequency of consumers’ posi-
tive emotions during the presentation of a video material.
In the same way, it measures negative emotions and the
periods in which consumers do not express any emotions
whatsoever. This process takes into account the position
of the brand in the context of an emotional reaction. Our
proprietary algorithm takes into account the dynamics of
emotions of viewers and their trend in the sections that
have an impact on the perception of advertising.
Insight Score is an advanced index, calculated according
to our developed formulas. To ensure the highest quality
of our services, it is constantly tested and improved. Its
big advantage is that it allows for effective evaluation
of advertising in terms of viewers, called the positive
emotional involvement. It reacts to changes in trends in
advertising and refers to the actual values of the market.
The higher the score, the higher the emotional involvement
of viewers while watching the ad and a more positive
response to branding. With Insight Score, we may not only
quickly and economically compare different versions of
the same ad, but also confront our own productions with
the materials produced by the competition.
max 7 max 7
PUBLIC
SECTOR
PURCHASE
1910
FIT
5,05
5,51
4,97
5,23
WOMEN
MEN
max 7 max 7
11
THE MEDIAN FOR
THE 29 ADS
24. Quantum Insight 24
QUANTUM INSIGHT APPLICATIONS
THE SURVEY PROCESS
An important factor in studies using the Quantum Insight
platform is the appropriate preparation of the condi-
tions in which they are held. The test is carried out in the
respondent’s home, and therefore, before starting the
test procedure, each of the test subjects is instructed by
a manual, especially developed for this purpose.
The ideal measurement conditions include:
• lack of back lighting,
• lack of side lighting,
• face set perpendicular to the camera,
• face clearly visible.
The Quantum Insight system includes triple control
of the measurement conditions. The observation of the
face on the screen allows the respondent to evaluate the
accuracy of the measurement conditions. Next, the system
automatically checks whether the test subject meets the-
se requirements and enables them to move to the target
phase of the study, or asks for a correction according to
the guidelines. During the study, the platform verifies the
quality of the detected face and asks the viewer for a cor-
rection of the head position or interrupts the test if there
is no re-adjustment to the conditions. Subsequently, the
video is sent to the respondent for the purpose of analysis.
fig. 4. Evaluation and control of the measuring conditions before and
during the study.
25. Quantum Insight 25
In developing the Quantum Insight platform, we set
ourselves the target to minimize, as much as possible,
the impact of negative conditions that may interfere with
the testing process. Accordingly, the artificial intelligence
algorithms are trained in effective emotion recognition
on both studio images and recordings failing to meet the
ideal requirements (among other things - in a room that is
badly lit, with subjects that are bearded, tattooed, wearing
glasses etc.). Quantum Lab has an extensive database of
facial images. Using Quantum Sense, so far more than 7.5
million video frames were analyzed, with more than 80,000
unique faces. We are constantly enlarging our resources.
fig.5. Using specially trained algorithms, Quantum Insight recognized
emotions also in tough test conditions, such as during the
consumption of meals by respondents, shading, facial hair or
glasses on their faces.
26. Quantum Insight 26
DATA ANALYSIS
The recording of each respondent is subjected to two
types of analysis: emotional and commitment. At the
beginning, we perform a classification of emotions in
individuals through a set of algorithms implemented in
Quantum Sense. The end result of this process is to iden-
tify the emotion that obtains the highest score in a given
period of time. Next, an aggregation of individual results
is performed - for the whole group of respondents, a per-
centage is calculated of specific emotions in the time in-
terval of 200 milliseconds.
The testing process includes situations in which the ca-
mera may “freeze” on the respondent, or instead of the-
ir own image they may produce pictures of, e.g. an actor,
model, etc. Were such recordings analyzed, it would gre-
atly upset the results. With the aim of processing only
valid sessions, we have created a special platform to mark
„suspicious sessions”. When the system detects such
a session, it sends an alert and stores it in a special di-
rectory. These recordings are subsequently analyzed by
an expert. This guarantees an analysis of only the correct
and selected sessions. These safeguards guarantee that
the results obtained are reliable.
EMOTIONAL INVOLVEMENT
The Quantum Insight platform also allows to determine the
emotional involvement of a respondent. This parameter
indicates the extent to which the material presented will, in
total, elicit any emotions (both as a percentage content in
the material and as a timing diagram).
27. Quantum Insight 27
FOCUSING
Using Quantum Sense, besides classifying emotions, we
are able to determine the position of the head of a subject,
relative to the camera and the displayed material. Doshi (La-
boratory for Intelligent and Safe Automobiles, University of
California) has examined how the estimation of the posi-
tion of the head allows, among other things, to determine
what is the level of concentration of an individual. Based
on scientific reports, our own experiments and the know-
ledge of the human muscular system, we have learned to
measure the concentration of the viewer in the course of
a test. Focus (concentration) is basically about the identi-
fication of the respondent’s interest. The research by the
Technopole Brest-Iroise institute shows that the position of
the head varies, depending on the degree of concentration
(Ba SO, 2011). We also added a parameter allowing to deter-
mine whether the viewer looks at the displayed material in
the course of the study, which may indicate, among other
things, when they start to get bored or irritated while wat-
ching a video, and which moments of the material are the
most interesting for them.
PITCH
ROLL
YAW
28. Quantum Insight 28
PRESENTATION OF RESULTS
The aggregated and analyzed quantitative data are placed
on a chart in the form of curves. These curves represent
the emotions experienced throughout the research group.
Why the whole group, instead of user separately? The-
re are two reasons. First, we follow the principles of ethics
and data protection - the survey is anonymous and guaran-
tees privacy for every user. Secondly, we apply the achie-
vements of psychology and statistics - we know that each
of us is a unique individual. We are aware of the fact that
the subjects will exhibit different reactions to the mate-
rial viewed. In addition, we keep in mind the „white noise”
that appears on the records - respondents moving whi-
le watching the material, yawning, eating meals, etc. To
reduce the impact of these factors, we aggregate the emo-
tional responses of all respondents to one common result.
Thanks to this, the final result is objective.
The data obtained as described above is presented in
the form of: a line distribution chart of emotions in time,
the film integrated with a graph and a table.
fig.6. A chart displaying the results of emotional involvement, obtained from the study of advertising.
29. Quantum Insight 29
Each displays how long the viewers were expressing
positive emotions, how long for the negative emotions,
and for how long the material did not elicit any emotions.
In each case the result is also displayed in the form of a na-
tural number, specifying the level of emotional involvement.
It is possible to benchmark within the survey conducted by
the platform user, or / and within the market segment of
the advertised product and the overall comparison of all the
ads contained in the Quantum Insight database. The graph
indicates with separate colors the moment of exposure
of the brand name. The user has the ability to see what
exact emotions the audience felt during the presentation.
The Quantum Insight platform allows to filter the results.
Each bit of information may be viewed, among other things,
divided into age groups, sex, place of residence and origin
or occupation of the subjects.
30. Quantum Insight 30
QUANTUM INSIGHT FUNCTIONALITY
In addition to the primary function, which is to provide
the analysis of the emotions of the given material, the
QuantumInsightplatformprovidesvaluableinformationonthe
entire study, such as:
• whether an ad carries an appropriate emotional load,
• which scenes within an ad evoke the strongest emotions,
• when customers lose interest in an ad,
• how the presentation of the logo of the brand influences
the emotional reactions,
• how to optimally and effectively shorten the adverti-
sing spot,
• how to design the future ads.
The numerous technological advantages of Quantum Insight
include:
• the whole process is based on a web browser,
• intuitive setting and analysis of results,
• the option to change the scale of a diagram,
• creation and comparison of video variants,
• a transparent and comprehensive interpretation of
results (percentage of respondents who have experien-
ced a particular emotion, the average number of occur-
rences of emotions and their average duration),
• advanced filtering results with the option of comparing
selected recordings,
• a unified algorithm for calculating the quality index of
advertising - Insight Score,
• exporting the results in the form of video, infographics,
and CSV file,
• advertising benchmarking - enables to compare the per-
centage advantage of the analyzed ad over other ads,
• the option to add declarative questions or select and con-
figure them in a simple way, thanks to a built-in bank of
certified declarative questions,
• the ability to automatically order a panel of respondents
(and adaptation of the research group),
• platform and tool operates in two languages (English
and Polish).
31. THE FINAL WORD 31
THE FINAL WORD
Within 18 months of setting up the Quantum Insight plat-
form, the Quantum Lab services have been used by 24
customers. Our team of experts analyzed 804 hours of
YouTube videos (more than 100 million video frames). The
current number of respondents is 100908 people. So far,
we have examined 450 advertising campaigns and 600
video materials.
With decades of work by various research units and,
in large part, our own experts, we are able to continually
improve our technology and equip it with new compo-
nents that will be able to identify other factors of human
biology. As a result, it has the potential to exist in com-
mercial electronics, ‚intelligent’ transport systems, safety
and security, electronic assistants, as an aid in the study
of customer satisfaction, an element of decision support
systems for courtrooms, in employee recruitment and
much more. In general, wherever there is a need for fast
and automatic interpretation of human state / behavior.
We hope it will become a permanent part of our lives, hel-
ping and serving us.
32. AWARDS 32
WHERE WE APPEARED? WHO TALKED ABOUT US? WHAT HAVE WE WON?
AWARDS
33. Bibliography 33
• McDuff, D., Kaliouby, R., Senechal, T., Demirdjian, D., and Pi-
card, R. (2014). Automatic measurement of ad preferences
from facial responses gathered over the internet. Image and
Vision Computing, 32, 630-640.
• Cohen, N. Sebe, A. Garg, L. Chen, and T.S. Huang, (2003) Fa-
cial expression recognition from video sequences: Temporal
and static modeling. Computer Vision and Image Understan-
ding, 91(1- 2):160–187.
• Cohn, J., Ying-li Tian, B., Kanade, T., (2000). Recognizing ac-
tion units for facial expression analysis. In 2000 IEEE Com-
puter Society Conference on Computer Vision and Pattern
Recognition (CVPR’00) - Volume 1, June 2000.
• Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G.,
Kollias, S., Fellenz, W., Taylor, J.G, (2001) Emotion recogni-
tion in human-computer interaction. Signal Processing Ma-
gazine, IEEE, Volume: 18, Issue: 1, Jan 2001. Pages: 32 – 80
• Darwin, C. (1872). The Expression of the Emotions in Man and
Animals. London: John Murray.
• Ekman, P. (2001). Smiling. In Blakemore, C. & Jennett, S.
(Eds.), Oxfors Companion to the Body. London: Oxford Uni-
versity Press.
• Ekman, P. (2009). Become Versed in Reading Faces. Entre-
preneur. Retrieved from http://www.entrepreneur.com/ar-
ticle/200934.
• Ekman, P. (2009). Lie Catching and Micro Expressions. In
Martin, C. (Ed.), The Philosophy of Deception. New York, NY:
Oxford University Press
• Ekman, P. (2010). Darwin’s Compassionate View of Human
Nature. JAMA, 303(6), 557-558.
• Barlett, M., Littlewort, G., Fasel, I., Movellan. J., (2003). Real
time face detection and facial expression recognition: Deve-
lopment and applications to human computer interaction.
In Proceeding of the 2003 Converence on Computer Vision
and Pattern Recognition Workshop.
• Bența Kuderna-Iulian and Vaida Mircea-Florin, (2015) Towards
real-life facial expression recognition systems, Advances in
Electrical and Computer Engineering, vol. 15(2), pp. 93–102.
• Berger, J., Milkman, K. (2012). What Makes online Content Vi-
ral? Journal of Marketing Research, Vol. 49, No. 2, pp. 192-205.
• Bible, E. (2009). Smiles and frowns are innate, not learned.
Retrieved from San Francisco State University: http://www.
sfsu.edu/news/2009/spring/1.html
• Bradley, M. M., Greenwald, M. K., Petry, M. C., Lang, P. J.
(1992). Remembering pictures: Pleasure and arousal in me-
mory. Journal of Experimental Psychology: Learning, Me-
mory, & Cognition 18 (2): 379–390.
• Brockmole, J., Henderson, J., (2005), Prioritization of New
Objects in Real-World Scenes: Evidence From Eye Movements,
Journal of Experimental Psychology: Human Perception and
Performance 2005, Vol. 31, No. 5, 857–868
• Brown, M., (2009). Should My Advertising Stimulate an Emo-
tional Response? Millward Brown: Knowledge Point.
Bibliography
34. Bibliography 34
• Goleman, Daniel (1995). Emotional intelligence. New York:
Bantam Books.
• Graves, J., Robinson, J., II (1976) Proxemic Behavior as a Func-
tion of Inconsistent Verbal and Nonverbal Messages. Journal
of Counseling Psychology, Vol. 23, No. 4, 333-338
• Kaliouby. P., Kaliouby, R., (2003). Real time facial expression
recognition in video using support vector machines. In ICMI
’03: Proceedings of the 5th international conference on Mul-
timodal interfaces, pages 258–264, New York, NY, USA, ACM.
• Kemeney, M. E., Foltz, C., Cavanagh, J. F., Cullen, M., Gie-
se-Davis, J., Jennings, P., Ekman, P. (2012). Contemplative
emotion training reduces negative emotional behaviorand
promotes procial responses. Emotion, 12(2), 338-350.
• McDuff, D., El Kalioubi, R., Cohn, J. F., & Picard, R. (Accep-
ted with revisions). Predicting ad liking and purchase intent:
Large-scale analysis of facial responses to ads. IEEE Trans-
actions on Affective Computing.
• Pantic, M., Rothkrantz, L., (2000). Automatic analysis of fa-
cial expressions: the state of the art. IEEE Trans. PAMI, 22(12):
1424–1445.
• TenHouten,W.D. (2007). General Theory of Emotions and So-
cial Life. Routledge.
• Teixeira, T., Wedel, M., and Pieters, R. (2012). Emotion-indu-
ced engagement in internet video ads. Journal of Marketing
Research, Vol. 49, No. 2, pp. 144-159.
• Tsiamyrtzis, P., Dowdall, J., Shastri, D., Pavlidis, I. T., Frank,
M. G., & Ekman, P. (2007). Imaging Facial Physiology for the
Detection of Deceit. Internetional Journal of Computer Vi-
sion, 71(2), 197-214.
• Ekman, P. (2016). What Scientists Who Study Emotion Agree
About 11(1), 31-34.
• Ekman, P., Friesen, W. V. (2003). Unmasking the Face. Cam-
bridge: Malor Books., p.169
• Ekman, P., Friesen, W. V., (1978) W. V. Facial Action Coding
System: A Technique for Measurement of Facial Movement.
Consulting Psychologists Press Palo Alto, California.
• Ekman, P., Friesen, W., Hager, J., (2002), Facial action coding
system, A Human Face.
• Ekman, P., Friesen, W.V. (1971). Constants across cultures in
the face and emotion. Journal of Personality and Social Psy-
chology 17 (2):
• Ekman, P., O’Sullivan, M. (2006). From Flawed Self-Asses-
sment to Blatant Whoppers: The Utility of Voluntary and In-
voluntary Behavior in Detecting Deception. Behavioral Scien-
ce and the Law, 24(5), 673-686.
• Ekman, Paul (1999). Basic Emotions. In T. Dalgleish and M. Po-
wer. Handbook of Cognition and Emotion. Sussex, UK: John
Wiley & Sons, Ltd.
• Ekman, Pl, (1992). Facial Expressions of Emotion: An Old
Controversy and New Findings”. Philosophical Transactions
of the Royal Society (London) B335: 63–69
• Facial Action Coding System (FACS) and the FACS Manual”.
Face-and-emotion.com. Retrieved 2013-10-26.
• Frank, M. G., Matsumoto, D., Ekman, P. Sinuk, K., Kurylo, A.
(2008). Improving the Ability to Recognize Micro Expressions
of Emotion. In Review.
• Godavarthy, Sridhar. (2011). „Microexpression spotting in vi-
deo using optical strain”. Web. Retrieved 15 June 2011.