# Using Big Five Personality Model to Detect Cultural Aspects in Crowds

Rodolfo Favaretto, Leandro Dihl, Soraia Raupp Musse  Pontifical Catholic
University of Rio Grande do Sul
Graduate Studies on Computer Science
Porto Alegre, RS, Brazil
Email<EMAIL_ADDRESS>Felipe Vilanova and Angelo Brandelli Costa
Pontifical Catholic University of Rio Grande do Sul
Graduate Studies on Psychology
Porto Alegre, RS, Brazil
Email<EMAIL_ADDRESS>
###### Abstract

The use of information technology in the study of human behavior is a subject
of great scientific interest. Cultural and personality aspects are factors
that influence how people interact with one another in a crowd. This paper
presents a methodology to detect cultural characteristics of crowds in video
sequences. Based on filmed sequences, pedestrians are detected, tracked and
characterized. Such information is then used to find out cultural differences
in those videos, based on the Big-five personality model. Regarding cultural
differences of each country, results indicate that this model generates
coherent information when compared to data provided in literature.

##  I Introduction

Crowd analysis is a phenomenon of great interest in a large number of
applications. Surveillance, entertainment and social sciences are examples of
fields that can benefit from the development of this area of study. Literature
dealt with different applications of crowd analysis, for example counting
people in crowds [1, 2], group and crowd movement and formation [3, 4, 5, 6]
and detection of social groups in crowds [7, 8, 9, 10]. Normally, these
approaches are based on personal tracking or optical flow algorithms, and
handle as features: speed, directions and distances over time. Recently, some
studies investigated cultural difference in videos from different countries.
Chattaraj et al. [11] suggested that cultural and population differences could
produce deviations in speed, density and flow of the crowd. Favaretto et al.
[12] discussed cultural dimensions according to Hofstede analysis [13] and
presented a methodology to map data from video sequences to the dimensions of
Hofstede cultural dimensions theory.

In this paper, we propose to detect crowd-cultural aspects based on the Big-
five personality model (or OCEAN) [14] (Brazilian version) from NEO PI-R [15]
using individuals behaviors automatically detected in video sequences. For
this, we used the NEO PI-R [14] that is the standard questionnaire measure of
the Big-Five Factor Model. The questionnaire provides a detailed personality
description that can be a valuable resource for a variety of professionals. We
firstly selected NEO PI-R items related to individual-level crowd
characteristics and the corresponding factor, as described later in this
paper. For example: ”Like being part of crowd at sporting events”
corresponding to the factor “Extroversion”. More details about personality
models are discussed in Section II.

After the NEO PI-R items selection (related to crowds characteristics), we
propose a way to map data extracted from video sequences to Big-Five
parameters, as described in Section III.

Since there are different distributions of each of the Big-Five factors in
different countries [14], we hypothesize that it would be possible to detect
cultural differences from videos processing crowd behavior from different
countries. This discussion is addressed in Section IV. Conclusions and future
work are presented in Section V.

##  II Related Work

This section discusses some topics concerned with personality and also
associated with crowd simulation.

Personality may be labeled as deep psychological individual level trait [16].
Trait is an inference made after observed behaviors that seeks to explain its
regularity [17]. In general, researchers agree that there are five robust
orthogonal traits which effectively matched personality attributes [18], known
as the Big Five: Openness to experience (“the active seeking and appreciation
of new experiences”); Conscientiousness (“degree of organization, persistence,
control and motivation in goal directed behavior”); Extraversion (“quantity
and intensity of energy directed outwards in the social world”); Agreeableness
(“the kinds of interaction an individual prefers from compassion to tough
mindedness”); Neuroticism (how much prone to psychological distress the
individual is) [19]. The development of the Big Five personality model has its
roots in the work by Allport and Odbert (1936) who tried to identify
individual differences extracting relevant words in the Webster’s Unabridged
Dictionary. They worked with the hypothesis that the most important individual
differences would be coded in language, since as they are the most important,
there would be an evolutionary necessity to communicate it. Although Allport
and Odbert (1936) found 4.500 words which referred to generalized and stable
personality traits, their technique couldn’t originate few personality traits
which explained most part of behaviors variance.

Raymond Cattel is commonly referred as the one who developed the methodology
which permitted the objective grouping of hundreds of trait descriptors in a
set of higher level factors [18]. Cattell [20] developed a taxonomy of
individual differences that consisted of 16 primary factors and 8 second-order
factors. Nevertheless, attempts to replicate his work were unsuccessful [21]
and researchers agreed that only the 5-factor model matched his data,
originating the Big Five personality model.

The NEO PI-R [15] is one of the most used instrument based on the Big Five
personality theory. It assesses the normal adult personality and is
internationally recognized as a gold standard for personality assessment. One
of its advantages is that it further specifies six facets within each
personality trait and have data from several countries which easily allows
cross-cultural comparisons [22, 23]. Although the current empirical evidence
matching individual level traits, such personality and crowd behavior is not
strong (one of the few examples is [24]), the Big-Five personality model is
widely used to model computational crowd simulation [25, 26, 27]. The model
allows to simulate a crowd with individual level parameters based on the
expected behaviors of the agents.

Recently, research has shown that digital records can be an effective tool in
predicting personality traits. Facebook likes, for example, can predict the
actual score of the Big-Five personality model, especially the Openness trait
[28], providing roughly as much information as the self-reported personality
test score itself. This makes room for the use of computational methods in
predicting an individual’s personality as effectively as through the analysis
of self-reported scores. A computational method to assess personality score
can be also useful since there are issues concerning traditional self-report
techniques: 1) individuals may deceive themselves and unintentionally distort
their ratings of socially desirable traits in a positive direction [29]; 2)
individuals can fake their responses to personality measures, especially in
contexts which the test is used as a selection criterion, such as in job
interviews; 3) individuals can distort their answers in different levels and
ways, making it harder to apply a general statistical correction which serves
equally to everyone [30].

One effective alternative to the self-report method is the observer ratings of
personality (i.e., acquaintances, friends, colleagues). A meta-analysis has
shown that observational rating provides substantial incremental validity over
self-reports of personality [30]. One of the possible reasons for it is that
self-reports assess the internal dynamics of an individual, whereas observer
ratings analyze the behavioral performance. As the behavior is a better
predictor of the future performance than the inner dynamics of an individual
[31] it might be the reason of the better predictive validity. Therefore, we
propose that it is possible to predict facets of personality traits of
individuals through computer vision of crowd behavior as effectively as
through the self-report method and observer ratings such as a collegue or a
friend. The rationale behind this proposal is that since observer ratings
might be as valid as the self-report, computer vision might be effective as
well - since the behavioral component is being analyzed and not solely the
inner dynamics of an individual. One example is the way we can successfully
predict players’ personality scores through behavioural cue of their avatars
in virtual worlds and games [32, 33].

Concerning cultural simulation, Lala et al. [34] introduced a virtual
environment that allows the creation of different types of cultural crowds.
The crowd parameterization is based on the cultural dimensions presented by
Hofstede [35]. The work proposed by Kaminka [36] presents data that aim to
differentiate populations with regard to their behavior of movement in crowds.
Cultural parameters are proposed and analyzed in videos from different
countries, for later comparison. Some of the analyzed parameters are: speed,
personal space, collision quantities and population flow.

In this paper, the idea is to map parameters from individual behaviors
(automatically detected from video sequences of different countries) to
generate a Big-Five personality model score (OCEAN) [14] for each of them. In
this sense, our contribution is a model based on a set of equations that
handle the individual parameters related to crowd behaviors obtained from
videos and mapped to crowd-related Big-Five personality traits, generating
profiles of each individual/analyzed video. Since personality differences in
the Big-five model between countries are established in the literature [22,
23], one can compare each specific result extracted from the video with the
related country/cultural score.

##  III The Proposed Approach

Our model presents two main steps: video data extraction and cultural
analysis. The first step aims to obtain the individual trajectories from
observed pedestrians in real videos. Using these trajectories, we extracted
data that are useful for the second step, that is responsible for the
personality and cultural analysis.

###  III-A Individuals Data Extraction

Initially, the information about people from real videos is obtained using a
tracker [37] to recover people trajectories. The features are following
described. We compute firstly the geometric information for each person $i$ at
each timestep: i) 2D position $x_{i}$ (meters); ii) speed $s_{i}$
(meters/frame); iii) angular variation $\alpha_{i}$ (degrees) w.r.t. a
reference vector $\vec{r}=(1,0)$. In addition, three other features are also
computed: iv) collectivity $\phi_{i}$, v) socialization $\vartheta_{i}$ and
vi) isolation levels $\varphi_{i}$. These features were chosen because two
reasons: Firstly, they are strongly related with the questions concerned with
groups activities in Neo-Pi survey [14]. The second reason is the theory
behind socialization/isolation that easily can be represented through
geometric data (positions and distances), and collectivity that has been
already explored in the context of crowd behaviors detection [4].

To compute the collectivity affecting one individual $i$ from all $n_{i}$
individuals in his/her social space (as presented in [38]), we used Equation
1:

$\phi_{i}=\sum_{j=0}^{n-1}\gamma e^{(-\beta\varpi(i,j)^{2})},$ (1)

where the collectivity between two individuals $i$ and $j$ is calculated as a
decay function of
$\varpi(i,j)=s(s_{i},s_{j}).w_{1}+o(\alpha_{i},\alpha_{j}).w_{2}$, considering
$s$ and $o$ respectively the speed and orientation differences between the two
people and $w_{1}$ and $w_{2}$ are constants that should regulate the offset
in meters and radians. We have used $w_{1}=1$ and $w_{2}=1$. So, values for
$\varpi(i,j)$ are included in interval $0\leq\varpi(i,j)\leq 4.34$. $\gamma=1$
is the maximum collectivity value when $\varpi(i,j)=0$, and $\beta=0.3$ is
empirically defined as decay constant. Hence, $\phi_{i}$ is a value in the
interval $[0;1]$.

To compute the socialization level $\vartheta$ we use a classical supervised
learning algorithm proposed by Moller [39]. The artificial neural network
(ANN) (illustrated in Figure 1) uses a Scaled Conjugate Gradient (SCG)
algorithm in the training process to calculate the socialization
$\vartheta_{i}$ level for each individual $i$.

Figure 1: Neural network used to learn the socialization level.

As described in Figure 1, the ANN has 3 inputs (collectivity $\phi_{i}$ of
person $i$, mean Euclidean distance from a person $i$ to others
$\bar{d_{i,j}}$ and the number of people in the Social Space111Social space is
related to $3.6$ meters [17]. according to Hall’s proxemics [17] around the
person $n_{i}$). In addition, the network has 10 hidden layers and 2 outputs
(the probability of socialization and the probability of non socialization).
The final accuracy from the training processes was 96%. We used 16.000 samples
(70% of training and 30% of validating). These samples were obtained from the
25 initial frames from each of the videos from our dataset. The remaining
frames were used to test the ANN as described in Section IV.

The ground truth (GT) was generated as follows: Firstly, we define if a person
has a high socialization level $GT\\_\vartheta_{i}$ based on Hall’s proxemics,
calculated according to the Equation 2:

$GT\\_\vartheta_{i}=\left\\{\begin{array}[]{ll}0,&\text{~{}if~{}}n_{i}=0\\\
\frac{n_{i}}{\rho},&\text{~{}otherwise}\end{array},\right.$ (2)

where $n_{i}$ is the number of individuals in the social space around the
person $i$ and $\rho$ is the number of individuals in the analyzed frame. If
$GT\\_\vartheta_{i}>=0.5$, we considered this person as a “social” person,
otherwise the person is considered “not social” in the training processes.
Secondly, we proceed a visual inspection manually correcting false positives
or false negatives in comparison to our personal opinion. Using this GT and
the neural network we evaluate $\vartheta_{i}$ for each individual $i$ at each
frame, for each video in the test group.

Once we get the socialization level $\vartheta_{i}$, we compute the isolation
level $\varphi_{i}=1-\vartheta_{i}$, that corresponds to its inverse.

Finally, for each individual $i$ in a frame $f$ of a certain video $v$, we
will have a features vector
$\vec{V_{i}^{f,v}}=\left[x_{i}^{f,v},s_{i}^{f,v},\alpha_{i}^{f,v},\phi_{i}^{f,v},\vartheta_{i}^{f,v},\varphi_{i}^{f,v}\right]$.
Then, computing the average for individual $i$, for all frames of a video $v$,
we will have vector $\vec{V_{i}^{v}}$ for each person $i$.

In this paper, we are interested about mapping the features vector from each
individual in a specific video $\vec{V_{i}^{v}}$ to OCEAN dimensions, detailed
in next section.

###  III-B Mapping crowd features in Cultural Dimensions

Our goal is to map data from $\vec{V_{i}}$ to $\vec{BF_{i}}$, where the last
one is related to the Big-Fve dimensions (or OCEAN) for each individual $i$
for a certain video and described as a features vector:
$\vec{BF_{i}}=\left[O_{i},C_{i},E_{i},A_{i},N_{i}\right]$.

Therefore, in our method $\vec{BF}$ is computed based on NEO PI-R. With human
beings, OCEAN is calculated based on their answers to the full version of NEO
PI-R, with $240$ items. Our goal is to find out NEO PI-R “answers” for each
individual in the video sequence, based on their features ($\vec{V_{i}}$). So,
we have proposed a series of empirically defined equations to map individual
and crowd characteristics (in video sequences) to OCEAN cultural dimensions.

As stated before, the complete version of NEO PI-R has $240$ items. Firstly,
we selected 25 items from NEO PI-R inventory that had a direct relationship
with crowd behavior. From the 25 items selected, 18 (72%) are from
Extroversion, 3 (12%) are from Neuroticism, 2 (8%) are from Agreeableness, 1
(4%) is from Openness and 1 (4%) is from Conscientiousness. One example of
items presented in NEO PI-R is “1 - Have clear goals, work to them in orderly
way” and possible answers are in the interval [0;4] which respectively
represent: Strongly Disagree, Disagree, Neutral, Agree and Strongly Agree.

Our proposal is to answer these 25 items (see Table I) for each individual at
each frame in the video through the Equations on the right in Table I. For
example, in order to represent the item “1 - Have clear goals, work to them in
orderly way”, we consider that the individual $i$ should have a high velocity
$s_{i}$ and low angular variation $\alpha_{i}$ to have answer compatible with
4. So the equation for this item is $Q_{1}=s_{i}+\frac{1}{\alpha_{i}}$. In
this way, we empirically defined equations for all 25 items, as presented in
Table I.

TABLE I: Equations from each NEO PI-R item selected.

NEO PI-R Item Equation 1 - Have clear goals, work to them in orderly way
$Q_{1}=s_{i}+\frac{1}{\alpha_{i}}$ 2\. Follow same route when go somewhere
$Q_{2}=\alpha_{i}$ 3\. Shy away from crowds $Q_{3-8}=\varphi_{i}$ 4\. Don’t
get much pleasure chatting with people 5\. Usually prefer to do things alone
6\. Prefer jobs that let me work alone, unbothered 7\. Wouldn’t enjoy holiday
in Las Vegas 8\. Many think of me as somewhat cold, distant 9\. Rather
cooperate with others than compete $Q_{9-10}=\phi_{i}$ 10\. Try to be
courteous to everyone I meet 11\. Social gatherings usually bore me
$Q_{11}=\varphi_{i}+std(\alpha_{i})$ 12\. Usually seem in hurry
$Q_{12}=s_{i}+\alpha_{i}$ 13\. Often disgusted with people I have to deal with
$Q_{13}=\varphi_{i}+\frac{1}{\phi_{i}}$ 14\. Have often been leader of groups
belonged to $Q_{14}=\phi_{i}+\vartheta_{i}+\frac{1}{\alpha_{i}}$ 15\. Would
rather go my own way than be a leader $Q_{15}=\frac{1}{Q_{14}}$ 16\. Like to
have lots of people around me $Q_{16-21}=\vartheta_{i}$ 17\. Enjoy parties
with lots of people 18\. Like being part of crowd at sporting events 19\.
Would rather a popular beach than isolated cabin 20\. Really enjoy talking to
people 21\. Like to be where action is 22\. Feel need for other people if by
myself for long $Q_{22-25}=\vartheta_{i}+\phi_{i}$ 23\. Find it easy to smile,
be outgoing with strangers 24\. Rarely feel lonely or blue 25\. Seldom feel
self-conscious around people

Once all questions $k$ (in the interval $[1;25]$) have been answered for all
individuals $i$, we will have $\vec{Q_{i,k}^{f}}$ for each frame $f$. We
computed the average values to have one vector $\vec{Q_{i,k}}$ per video.

As already mentioned, NEO PI-R items answers vary from $0$ to $4$. We
converted the values obtained in $\vec{Q_{i,k}}$ in one of the 5 score
possible options (0, 1, 2, 3 and 4) by simply normalizing the answers in 5
uniformly distributed levels, since we know the maximum level for each item at
each video. We called this normalized vector as $\vec{Q^{\prime}_{i,k}}$. In
NEO PI-R definitions, some questions should invert the values, because an item
score 4 (Strongly Agree) can represent a high value of Extroversion or low,
depending on the question. For example, let us analyze questions 4 and 16. A
score=4 to both of them represents completely opposite answers in terms of
sociability. So, to get the correct values, we applied a factor to the
questions which score should be inverted:
$\vec{Q^{*}_{i,k}}=4-\vec{Q^{\prime}_{i,k}}$.

In addition, in NEO PI-R definition, each of the questions
$\vec{Q^{\prime}_{k}}$ are associated to one of the Big Five dimensions, as
shown in next equations:

$O_{i}=\frac{Q^{*}_{i,2}}{\varrho},$ (3)
$C_{i}=\frac{Q^{\prime}_{i,1}}{\varrho},$ (4)
$E^{\prime}_{i}=Q^{\prime}_{i,3}+Q^{\prime}_{i,12}+Q^{\prime}_{i,14}+\sum_{q=16}^{23}Q^{\prime}_{i,q},$
(5) $E^{*}_{i}=\sum_{q=4}^{8}Q^{*}_{i,q}+Q^{*}_{i,11}+Q^{*}_{i,15},$ (6)
$E_{i}=\frac{(E^{\prime}_{i}+E^{*}_{i})}{\varrho},$ (7)
$A_{i}=\frac{\sum_{q=9}^{10}Q^{\prime}_{i,q}}{\varrho},$ (8)
$N_{i}=\frac{Q^{\prime}_{i,13}+\sum_{q=24}^{25}Q^{*}_{i,q}}{\varrho},$ (9)

where $\varrho$ represents the percentage of questions from the total, in each
dimension (O, C, E, A and N), respectively 4%, 4%, 72%, 8% and 12%, as
explained previously.

Once we get the OCEAN values of each person, we calculate the OCEAN of the
video by the mean of people’s OCEAN. In a similar way, the OCEAN of a country
is the mean of videos from that country. In the next section we present some
obtained results of our method.

##  IV Experimental Results

In this section we discuss some results obtained with our approach. We
evaluated our method in a set of $20$ videos from $4$ countries ($9$ from
Brazil, $5$ from China, $3$ from Austria and $3$ from Japan). These videos,
with a duration varying between 100 and 900 frames, were collected from
different public databases available on the Internet, such as [4, 40, 41].
Firstly, we get the OCEAN of each individual in the scene (Figure 2 shows some
examples). In Figure 2 (a) we can observe the higher E that was found in an
individual, part of a group of people, while the opposite happens in (b) when
lower E was computed for individual alone and far from the others.

(a) Higher E (Brazil)

(b) Lower E - Higher N (China)

(c) Higher A (Brazil)

(d) Lower A (China)

(e) Higher O (Japan)

(f) Lower O (Brazil)

Figure 2: Examples of some individuals OCEAN levels: a) the highlight person
has the highest Extraversion, b) shows the person with the lowest Extraversion
(and highest Neuroticism), c) shows the person with the highest Agreeableness
and the person highlighted in d) has the lowest Agreeableness. The highlight
person in e) has the highest Openness and the person highlighted in d) has the
lowest Openness. Figure 3: OCEAN comparison between our approach and
literature values.

Same kind of analysis can be done for images (c) and (d) relating to their
collectivity (higher and lower respectively) as described in Equation 1.
Although it is more difficult to visual inspect the dimensions O, C and N we
present the qualitative results. For example in Figure 2 (e) the highlighted
individual has lower angular variation in comparison to all others (higher O
value), while in (f) this is the individual with higher angular variation,
consequently having lower value of O. In addition, in Figure 2 (b) we obtained
the higher value of N, since it is dependent of the inverse of collectivity
and socialization. Once the individual OCEAN values are computed, we get the
mean OCEAN value for each video. The country’s OCEAN, in turn, is calculated
by the average OCEANs of that country’s videos.

Figure 4: OCEAN results from Brazil.

Figure 4 shows the results obtained by the country Brazil in all OCEAN
dimensions, in comparison with the literature [14], considered as ground-truth
in our approach. It is interesting to highlight that results achieved for this
country showed the higher accuracy, when compared to the other countries (see
the Figure 3). This was the country with more available videos to be processed
in our method (9 videos), in comparison with other countries.

In addition, we computed the perceptual error when accumulating each dimension
from all videos and compared with literature for those Countries. Figure 6
shows such errors and also indicates that the presented error of dimension E
has lower value; that is an interesting observation since this was the
dimension that had more questions to be analyzed, as shown in Equations 5, 6
and 7.

In terms of cultural aspects of individuals in the videos, Table II shows the
countries that get the higher and lower values in each dimension, according to
our approach. For example, Brazil is the most extrovert country, while the
less neurotic is Japan.

TABLE II: Countries with Higher and lower values in each Big Five dimension.

O C E A N Higher China Austria Brazil Japan Austria $(0.89)$ $(0.53)$ $(0.50)$
$(0.60)$ $(0.52)$ Lower Brazil Japan China China Japan $(0.53)$ $(0.30)$
$(0.33)$ $(0.51)$ $(0.42)$

According to previous work, another classical cultural dimension is proposed
by Hofstede [13]. In a recent paper, Favaretto et al. [12] presented the
cultural aspects of people in video using Hofstede’s cultural dimensions
theory (Figure 5). We compared our error using Big-Five (Figure 6) with this
method, when using Hofstede’s.

Figure 5: Hofstede percentual of differences. Figure 6: Big-Five percentual of
differences.

The accuracy of each approach (OCEAN and Hofstede) can be found in terms of
the mean difference percentual when compared with the literature results,
considering all dimensions among all videos. With an average error of 30% from
the results presented in literature, the OCEAN method proved to be more
promising than Hofstede (with an average error of 53%) for culturality
mapping.

It is important to note that the mapping to OCEAN dimensions was empirically
defined through equations using data extracted from computer vision. NEO PI-R
measured these dimensions by considering a different type of information
(subjective responses of individuals collected through questionnaires).

In this sense, it is possible to affirm that, even with few videos used, the
results obtained with the proposed approach are coherent with NEO PI-R results
and more effective if compared with Hofstede dimensions. The factor
Extroversion (E) is the one that seems to be more predictable with our model.
Probably because this factor comprehends the majority of items related to
crowd behaviors.

##  V Conclusions

In this paper we described a way to map equations to compute individual-level
traits from video sequences, based on individuals and groups features. Our
model computed, from video sequences, OCEAN personality traits and compared
with data from different countries existent in the literature. In addition, we
compared with some previous work that computed Hofstede dimensions using a
similar approach. We believe the results are promising and video sequences can
be used to detect crowd cultural aspects.

For our future work we intend to validate our model asking participants how
much they agree with the assigned score of each item in the Big-Five
questionnaire that resulted in our model for individuals with high scores in
selected videos. By doing this we can compare human score with computer
generated score of the same videos.

We also intend to increase our set of video data. Both aspects, number of
countries and the among of videos from each of them, should be considered. One
of the major difficulties of this work was to find a suitable set of videos to
perform the experiments.

In addition, we intend to make video-recordings of group situations where each
individual presented in the video has previously evaluated OCEAN scores. For
this, one plausible option is evaluate our method with the SALSA dataset [42],
which provides Big-five personality traits for a group of people in video
sequences.

We may thus have another evidence of the validity of the presented model. In
addition, we plan to create a new model comprehending different psychological
domains related to crowd characteristics that have documented cultural
differences: extraversion from the big-five model [14], Hofstede’s
collectivism [13], Hall´s personal space [43], fundamental diagram [11] and
the subjective pace of time [44].

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