=Paper=
{{Paper
|id=Vol-1622/SocInf2016_Paper2
|storemode=property
|title=Social Influence Analysis based on Facial Emotions
|pdfUrl=https://ceur-ws.org/Vol-1622/SocInf2016_Paper2.pdf
|volume=Vol-1622
|authors=Pankaj Mishra,Rafik Hadfi,Takayuki Ito
|dblpUrl=https://dblp.org/rec/conf/ijcai/MishraHI16
}}
==Social Influence Analysis based on Facial Emotions==
https://ceur-ws.org/Vol-1622/SocInf2016_Paper2.pdf
Proceedings of the 2nd International Workshop on Social Influence Analysis (SocInf 2016)
July 9th, 2016 - New York, USA
Social Influence Analysis based on Facial Emotions
Pankaj Mishra, Rafik Hadfi, and Takayuki Ito
Department of Computer Science and Engineering
Nagoya Institute of Technology, Gokiso, Showa-ku, Nagoya, 466-8555 Japan
{pankaj.mishra, rafik}@itolab.nitech.ac.jp,
ito.takayuki@nitech.ac.jp
Abstract. In this paper, we propose a method to analyse the social correlation
among the group of people in any small gathering; such as business meetings,
group discussion, etc.; Within such networks, correlation is build based on the
tracked facial emotions of all the individuals in the network. The facial emotional
feature extraction is based on active appearance model; whereas the approach for
emotion detection lies in the dynamic and probabilistic framework of deep be-
lief networks. Combining active appearance model with deep belief networks for
emotion recognition gives higher recognition performance level compared with
other methods. The analysis of change in facial emotions of all the individuals in
the group help us to understand the hidden correlation among them, which is not
observable with the naked eyes. Finally, we evaluate the system by comparing the
results with ground truth of a scripted discussion. Also, the results obtained by
our system effectively reflects the emotion propagation in the scripted discussion.
1 Introduction
In the recent years, studying the social relationship among the group of people has
become an interesting topic. However, analysing the social relationship among the indi-
viduals, in the social network is the challenging task; wherein, multiple people need to
be tracked in the real work environment. In any workgroup, people have the tendency
to come together and form the virtual groups and then the actions of the influential per-
son are propagated to others in this virtual group. The goal of this work is to analyse
the ways in which these group members induce their actions (emotion, speech, etc.) to
other members. The analysis of social behaviour propagation in a social network is the
integration of social theories with computational methods. For instance, [3] proposed
a system to analyse the social interactions in real working environments by tracking
the human location and pose of body. Similarly in the work [18], analysis is based on
tracking speaking and writing patterns. Moreover, the propagation of influence in huge
social networks, like Twitter or Facebook, are extensively studied; wherein, online ac-
tivities of the users are tracked, as discussed in work [1]. From the existing works, it
Copyright c 2016 for the individual papers by the papers’ authors. Copying permitted for private
and academic purposes. This volume is published and copyrighted by its editors.
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can be said that, the analysis of social correlation focuses only on tracking pose, user
activities, etc. However, very less attempt were made to analyse the social correlations,
on the basis of the facial emotions.
In this paper, we propose a novel method to analyse the social correlation among
a group of people, which is based on the facial emotion of all the individuals. Since
the human’s facial emotion in any discussion can give the clearer insight of social re-
lationship between any two individuals; so our algorithm should discover the clearer
relationship among all the individuals. Also, in any small gatherings, the groupings
are done on the basis of concepts like homophily, cofounding or influence. Homophily
and cofounding concepts; are based on the background information of people, such as
profession, knowledge of the topic of discussion, age, etc. However, the analysis of in-
fluence is purely based on tracking the actions and the corresponding reactions among
the participants in the network; action or reaction could be a pose, dialogue, emotion,
etc. We focus on tracking the emotion and communication sequence of all the par-
ticipants, to analyse the influential correlation among them. The emotion detection is
implemented using the deformable model Active Appearance Model (AAM) [4] and
the deep learning algorithm: Deep Belief Network (DBN). Whereas, social correlation
was built based on the proposed algorithm discussed in the later sections. Briefly, we
propose an algorithm to discover the knowledge of the influential correlation among all
the participants in the network; wherein we used AAM and DBN for emotion detec-
tion and social relationships methods for emotion detection. This work could find many
applications; such as, negotiation, viral marketing, surveys, etc.
We have two main contribution: Firstly, emotion diffusion parameter was intro-
duced, which defines the strength of the emotion induction between any two nodes for
all instance of time. Another contribution is the definition of influential correlation in
terms of weights of weighted graph, which is associated to the orientation of influence
(’+’ or ’-’). The sign ’-’ or ’+’ represents the direction of the induced emotion. Apart
from this, the frontal video dataset of a scripted discussion, which was used in this paper
as a testing dataset, can also be adopted to build a similar baseline system. The rest of
the paper is organised as following. Section 2 provides an overview of the methodology
of our system along with the data pre-processing, the facial emotion detection, and the
social influential correlation building. Section 3 presents the experimental results that
validate our proposed algorithm. Finally, we conclude and provide the future work in
section 4.
2 Methodology
The overall architecture of our system is divided into two main modules: the facial
emotion detection (facial feature extraction and facial emotion recognition) and the
social influence detection. As mentioned before, the algorithm for building the social
correlation is based on the facial emotion of the individuals. So, in this system, video
data is of a business meeting or a group discussion is fed, which in turn gives the hidden
correlation among the individuals. Also, in order validate the algorithm, change in one’s
emotion with respect to other participants should be reflected. The final social influential
correlation among the individual is represented in the form of weighed graph; wherein,
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July 9th, 2016 - New York, USA
weights denotes the strength of the influence and sign denotes the orientation of the
influence. Firstly, the frontal video data of the discussion, needs to be pre-processed
before examining the correlations. The pre-processing includes framing of the video,
locating and cropping the frontal image in all the frames. Tracking the face frontal
was realised using the Viola-Jones algorithm [10]. These, processed frontal frames for
all the participants along with the communication sequence of all the participants in
each frame is recorded; where the communication sequence means, track whether the
individual is listener or speaker in a particular frame, which is based on the metadata of
the discussion. In the later subsections, we would discuss the two main modules of our
proposed system; the facial emotion detection and the social influence detection.
2.1 Facial Feature Extraction
In this section, the whole process to extract the facial features of all the individuals,
is being discussed. Then, in the next subsection, we will discuss about the emotion
recognition. Firstly, we locate the 68 facial feature points in the frontal frames; feature
points are nose, eyes, mouth and eyebrows. Then, around these detected facial points,
we extract the shape and appearance feature vectors. The shape feature vector denotes
the co-ordinates of all the facial features, whereas appearance features can be Gabor de-
scriptor, Local Binary Pattern (LBP) descriptor, etc as discussed in [8]. However, most
of the works in past, [4, 2, 12], were based on the deformable model, named Active ap-
pearance model (AAM). AAM is a computer vision algorithm to track the facial feature
points on human face. Additionally, AAM provides a compact statistical representation
of the shape and the appearance variation of the human face. So, a trained AAM was
used, to align the human face, and later to extract the shape and the appearance fea-
tures. The implementation of AAM was based on the Menpo [13] python libraries, and
was trained using the two publicly available database, FERA database [17] and LFPW
database [14]. Further, this trained AAM can be used to track the face, and to extract
the three types of holistic features around the 68 tracked landmark points. These three
holistic features are, similarity normalised shapes (s-pts), similarity normalised appear-
ance (s-app) and canonical appearance (c-app) [2]; where, s-pts being the shape feature
vector and s-aap and c-aap are the appearance feature vector; appearance feature vec-
tors are scale-invariant feature transform (SIFT) [11] descriptors. Further, in order to
reduce the AAM fitting iterations, the landmarks of the current frame were initialised
with the landmarks of the previous frame.
Facial Emotion Recognition The feature vectors from AAM tracking, is used for train-
ing a classifier, to classify the frames as per the carrying emotions in each frames. Ba-
sically, an emotion is recognised by the presence of one or more specific Facial Action
Units (FACS) [6]; the combination of these FACS defines one of the 7 basic emotions
namely, happy, sad, disgust, anger, contempt, surprise and fear. The DBN implemen-
tation was similar to the models discussed in [9, 16], except the number of the input
and output of DBN. The DBN was implemented using the Theano python libraries, we
adapted the Theano deep belief network code for MINST classification, to construct our
Deep belief network. Later, DBN was trained with the CK+ dataset [12]; where DBN
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Proceedings of the 2nd International Workshop on Social Influence Analysis (SocInf 2016)
July 9th, 2016 - New York, USA
takes combined feature vectors as input and one of the 7 emotions as the output. At the
end, if there is any abrupt emotion change or missing emotions, this may be because of
wrong prediction. So, we replace that emotion with the most recent emotion; consid-
ering both the past and future frames of the individual, thus increasing the recognition
accuracy. Now, this emotion labels associated with all the frames is used for correlation
building as discussed in the next subsections.
2.2 Social Influence
The social correlation among the connected group of people, can be explained mainly
by three phenomenon; homophily, cofounding and social influence. The concepts of ho-
mophily and cofounding are more related to the tendency of an individual to follow the
other individual, that have similar characteristics, profession, etc. However, analysing
the social influence is the tedious task, wherein, the actions of the individual can induce
the similar action on the most related individuals, such as: friends, collogue, etc. In our
proposed methodology, we track two different actions for influence analysis; namely,
communication sequence (whether the individual is speaking or listening) and facial
emotions. However, our correlation building algorithm is mainly based on the tracked
emotions. Whereas, the communication sequence (C) is used as a signal of interaction
initiation. Because, the interaction initiating individual is are more significant than the
non initiator, for tracking the correlation. The proposed algorithm for correlation anal-
ysis is based on the two data, namely, both the tracked actions (E & C) and background
information (I) of all the individuals (nodes).
The information database I consist of details; such as age, gender, profession, se-
niority, etc., for all the individuals in the network. As mentioned before influence is
build among the most related individuals or the individuals having the higher affinity
among them. In order to showcase the affinity (Aij ) between any two individual i and
j, we label all the individuals based on the their importance, a.k.a. node centrality. The
node centrality (γ) is calculated using the details in the information database (I) of the
network. Although, there are many different methods to calculate the value of γ in a
network, as discussed in [15]; in our considered domain, mentioned methods do not
suffice our purpose to rank the individuals. So, we decide γ for all the individuals on
the basis profiling and clustering the nodes based on the information in I. The intuition
behind the γ of an individual is, if one of the individual has the superior knowledge
in the topic of discussion, or is the senior member, then this individuals‘s opinion will
be most likely choice of the others. Also, if some individuals belong to same organisa-
tion or are colleagues, or have some family ties, than they tend to form implicit virtual
groups.
Basically, γ reflects the co-founding and homophily in the considered network; fur-
ther the γ could be set analysing the network‘s background information or in case of
larger networks, clustering of profiles or algorithms similar to customer profiling [5]
can be used. The whole method of social influence detection can be divided in two basic
steps (i) Emotion Propagation Tracking and (ii) Social influence Analysis, as discussed
in the next subsections.
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July 9th, 2016 - New York, USA
Emotion Propagation Tracking The emotion propagation tracking algorithm is based
on the recorded emotions of all the individual per second and node centrality (γ) for all
the individuals. In our work, we consider the emotion induction by tracking the commu-
nication sequence of all the individuals. That is, we track the emotion change of all the
individuals, when one of the individual is active (speaking) or inactive (listening). Apart
from this, we track the emotion of all the individual irrespective of active or inactive in-
dividuals. The first scenario is; action induces emotion (a → e) and the other scenario
is, emotion induces emotion (e → e). The a → e correlation is said to have occurred,
if the action of an node has influenced the emotion of the other nodes. Whereas, e → e
correlation is said have occurred when the emotion of an node influences the emotions
of the other nodes. The diffusion of emotions in the scenarios can be represented by an
emotion diffusion parameter (∆E), calculated using the equation 1,
τe × ωe
∆E = (1)
T
γ × te
ωe = (2)
Ne
where τe is the time interval of emotion e, and ωe is an node’s emotion coefficient
of e. T is the total frame considered for emotion diffusion calculation ∆E. The ωe is
calculated using equation 2, which is pre-calculated for all the nodes for every emotion.
The te is the total time instance for which node has an emotion e and Ne is the total
number of time instance when all the nodes has emotion e.
Other than this, we define the direction of the emotion diffusion by δ, which can be
1 or -1. The δ for five emotion neutral, happy, surprise, sad and angry are +1,+1,+1,-
1 and -1 respectively. These values are based on the discussion in [7]. The emotion
diffusion in both the scenarios is calculated using the algorithms 1 and 2. Let us first
discuss the algorithm 1 for a → e scenario, which accepts the list of passive (agent
with action: emotion or no emotion) agents for every active (agent with action: speech
or both) nodes, and gives ∆E 1 and δ 1 for all combinations of passive and active nodes
as output.
In the equation on line 8, we calculate the ∆E 1e values for each emotion e carried
by the node p for time τ e , during the interval Ta , for which the node a was active. Then,
the final ∆E 1 value is the maximum value in set ∆E 1e . Thus finding that emotion e
was induced by an node a on node p. Later final δ 1 is calculated by multiplying the δ of
a and e, which gives the direction of the emotion induction. Similarly, in the algorithm
2, emotion diffusion ∆E 2 and δ 2 are calculated for all the pairs of nodes, irrespective
of the active node, where T=1. At the end of this step, we get four values, ∆E 1 and
∆E 2 , denotes the emotion diffusion parameters and δ 1 and δ 2 , denotes the direction
of emotion diffusion values for all the pairs of nodes. Later, we build the influential
correlation among the nodes, as discussed in the next subsection.
Social influence Analysis In the algorithms 1 and 2, we get the emotion diffusions
∆E 1 , ∆E 2 , δ 1 and δ 2 for both the scenarios. Then, we calculate the influence of each
node on the other nodes, based on the algorithm 3. The objective is to find the social
influence amongst all the agents in the network, in terms of weight of the weighted
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Proceedings of the 2nd International Workshop on Social Influence Analysis (SocInf 2016)
July 9th, 2016 - New York, USA
Algorithm 1 Emotion Propagation (a → e)
1: procedure E MOTION D IFFUSION
2: Input: a: Active node
3: P: List of passive node
4: Output: ∆E 1 and δ 1 for all passive node
5: // where δ is −1 or 1
6: for all p enumerate(P) do
7: for all e Emotion E do
τ e ×ω e
8: {∆E 1e ap } ← apTa p
9: // Maximum of ∆E 1e is final ∆E 1
10: // δ e is emotion having maximum for ∆E 1e
11: ∆E 1 ap ← max({∆E 1e ap })
12: δ 1 ap ← δa × δ e p
13: End
Algorithm 2 Emotion Propagation (e → e)
1: procedure E MOTION D IFFUSION
2: Input: A: List of all node
3: B: List of all node
4: Output: ∆E 2 and δ 2 for all pair of nodes
5: (where δ is −1 or 1 )
6: for all a enumerate(A) do
7: for all b enumerate(B) do
8: for all e Emotion E do
e e
9: {∆E 2e ab } ← τ abT×ω b
10: (where T is 1 )
11: (Maximum of ∆E 2e is final ∆E 2 )
12: (δ e is emotion having maximum for ∆E 2e )
13: ∆E 2 ab ← max({∆E 2e ab })
14: δ 2 ab ← δa × δ e b
15: End
graph. The input to this algorithm is list of ∆E 1 , ∆E 2 , δ 1 and δ 2 for all the pairs of
nodes. Based on these values, the weight Wij between agent i and j is calculated using
the equation on line 7. Where, summation of ∆E 1 and ∆E 2 for i and j is divided by
summation of ∆E 1 for all the neighbouring nodes of i and j. Finally the orientation of
the influence is associated with the Wij , by multiplying δij . Thus, as a output we get a
weighted graph, wherein weight represents the influence and sign ’-’ and ’+’ represents
the orientation of the influence being induced.
3 Experimental Results and Discussion
In this section we describe the evaluation of our baseline system, which analyses the
social influence in the network based on analysing the facial expressions of all the par-
ticipants in the network. In order to test our proposed algorithm, we need a input frontal
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Proceedings of the 2nd International Workshop on Social Influence Analysis (SocInf 2016)
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Algorithm 3 Influence Calculation
1: procedure I NFLUENCE
2: Input: ∆E 1 and ∆E 2 for each pair of nodes
3: δ 1 and δ 2 for each pair of nodes
4: Output: W weight for each pair of nodes
5: for each pair of node i and j do
6: δij ← δ 1 ij × δ 2 ij
∆E 1 +∆E 2
7: Wij ← Pk ij ∆E 1 ij × δij
N (j) kj
8: // N (j) neighbours of node j
9: End
video data of small network to detect the emotions. However, such dataset was not
available, therefore we recorded a scripted discussion of five participants through video
conferencing, let us say them as participants A, B, C, D and E. In the scripted discus-
sion the role of each node is defined, also action-reaction of each pair of node for each
time stamp are pre defined to build the ground truth of the data. Additionally, the video
data should be able to showcase the influence caused by emotion and action (speaking).
Therefore, we structured the script where each participants does the action (speaks) for
approximately 90 seconds in a sequence, yielding a total of 450 seconds video. Further,
assuming that the participants A, B and C belongs the same organisation and D and E
belongs to other organisation. Also, A and D are seniors amongst their group and B
has the higher knowledge on topic of discussion. Based on these assumptions of senior-
ity, knowledge about the topic, etc, the γA , γB , γC , γD and γE were set as 2, 5, 4, 5
and 3 respectively. Similarly, the calculation of γ for any larger network can be done
by comparing the basic information of all the participants in the network. In the later
subsections, we will discuss the results and then validate our results.
3.1 Results
The input video of length 450 seconds is first converted into frames and other pre-
processing steps are done. From the trained AAM, we extract the three types of feature
vectors from the frontal frame. Whereas, the frontal of the frame is detected by the Voila
Jones algorithm [10]. The approximate dimensions of the extracted feature vectors;
s-pts, s-aap and c-aap are 136 (x&y coordinates of 68 landmark points), 27000 and
27000 (approximately) respectively. Then, these feature vectors are used to detect the
carrying emotion by each frame, by classifying it with a trained DBN classifier. In our
baseline system, we only considered 5 emotions including the neutral emotion; namely,
neutral, happy, sad, surprise and angry. After classification, we get the emotion label
associated with all the frames; table 1 list the count of emotion labels detected per
seconds for all the five persons in the discussion; the accuracy of the emotion detection
was found to be ±82% for all the five emotions. So if any irregularity, sudden change or
missing emotions found in the emotion labels of the frame, then we set the emotion of
the neighbour frames. Further, the social influential correlation was done on the basis
of these change in emotion labels for every second, considering both the scenarios.
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The calculated values of ∆E 1 and ∆E 2 are listed in the table 3 and 4. Using these
values we calculate the influence in network, based on the algorithm 3. Finally, the
result is represented in the form of a weighted graph, the weights are listed in table
2. The discussion about the results and validation of our work is discussed in the next
subsection.
Node Neutral Happy Surprise Angry Sad Node A B C D E
A 100 20 150 35 145 A 0 -0.81 0.75 0.3 0.27
B 45 190 75 100 40 B -2.4 0 3.13 -0.93 0.45
C 30 140 80 125 75 C 0.512 0.53 0 0.24 0.3
D 27 190 50 153 30 D 0.34 -0.372 0.33 0 2.5
E 58 140 80 112 60 E 0.3 0.22 0.114 1.38 0
Table 1: Emotion detected per seconds Table 2: Final Weights
Node A B C D E Agent A B C D E
A 0 1.8 0.9 0.4 0.8 A 0 2.5 1.2 0.8 0.5
B 2.5 0 2.2 1.5 1.2 B 2.8 0 2.5 1.2 0.8
C 0.8 1.5 0 0.7 0.4 C 1.2 1.5 0 0.2 1.2
D 0.6 1.2 0.4 0 3.2 D 0.8 1 0.7 0 2.8
E 0.8 0.8 0.2 1.8 0 E 0.4 0.6 0.2 1.8 0
Table 3: ∆E 1 Table 4: ∆E 2
3.2 Discussion
In order to validate our methodology, we compared our result with the ground truth
of the fed data; i.e.,emotion propagation (change in emotions) in the fed video. In our
work, influence was calculated considering the two environment, namely, a → e and
e → e. Figures 1 depicts the emotion propagation in a → e environment and e → e.
Both the scenarios figures depicts the change in emotion of each node A, B,C,D and E
represented in different colours with time. Where, y-axis denotes the detected 5 emo-
tion labels normalised along the axis with different emotion and x-axis denotes time
in seconds. Let us first consider emotion propagation in a → e environment depicted
in the figures 1a to 1e. From these figures, it can be said that change in emotion of B
induces an positive emotion change on C more often. Additionally B induces negative
emotion change on A, i.e., A is happy when B is sad. Similarly, it can be said that emo-
tions of E is more often influence by emotions of D. Further, in order to avoid decision
based on the locally induced emotion on the agents (i.e. when emotion propagation is
analysed for the time duration of 90 seconds when our participant was active), we also
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Proceedings of the 2nd International Workshop on Social Influence Analysis (SocInf 2016)
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considered induction of emotion in the e → e environment. Wherein, overall influence
for the whole 450 seconds is considered, irrespective of active agent. The emotion prop-
agation in the figure 1f depicts the e → e environment. From which, it can be said that,
B induces emotion on C and A. Also, induction of emotion in reverse sense is not re-
ciprocated from the graphical figure. Similar correlation is also observed between node
D and node E.
Now, from the obtained results in the table 2, which are basically the weights of a
weighted graph, it can be said that node B induces node A in the opposite orientation
with weight -2.4, whereas node B induces C to a greater extent; that is 3.13. Similar
correlation is observed between node D and node E. Node D induces node E with
weight 2.5; although, vice versa correlation is not observed in both the cases. Other
than this, node B induces node D in negative sense. It can also be said that, if the value
of weight is less, that means it has lower induction; then that edge or correlation can
be ignored or removed form the network. Thus, it will reduce the complexity of other
searching algorithms (if any) in the network. By analysing the obtained results, it can be
said that the emotion propagation in the scripted discussion is very well represented in
terms of weights of the weighted graph. Therefore, it can also be said that our algorithm
proves to be efficient to measure the influence in the network.
(a) Active Node: A (b) Active Node: B (c) Active Node: C
(d) Active Node: D (e) Active Node: E (f) No Active Nodes
Fig. 1: Emotion Propagation
4 Conclusion
We introduced a system that can track the emotions diffusion in a social network, which
can help in extracting the social influence correlation among the nodes of the network.
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Intuitively, the measure of social correlation gives an idea of how the change of emo-
tions of one person affects the emotions of another person. If any particular node in the
network brings a high degree of emotional change in the network, it can be said that this
node is an influential node in the network. Facial emotions were classified by training
a DBN network, on the basis of the facial features extracted using a trained deformed
model AAM; then, we build the influential correlation among them using the proposed
algorithm. The emotion diffusion parameter were calculated, which was used to build
the correlation in terms of weights of weighted graph. The proposed method was eval-
uated against the scripted discussion video in our laboratory, wherein our predicted
correlation were able to reflect the known ground truth in the scripted discussion.
As future work, we think of extending our method to larger real-life networks and
incorporating emotions from voice data along with facial emotions. The resultant emo-
tions can depict human correlation in a more accurate fashion. Apart from this, we plan
to build a concrete method to calculate node centrality, and building the social associa-
tion on any anonymous network using social network mining techniques.
Acknowledgement
This work has been partially supported by the project ”Large-scale Consensus Sup-
port System based on Agents Technology” in the research area “Intelligent Information
Processing Systems Creating Co-Experience Knowledge and Wisdom with Human-
Machine Harmonious Collaboration” of JST CREST projects.
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