An Evolutionary-based Neural Network for Distinguishing between

Genuine and Posed Anger from Observers’ Pupillary Responses

Fan Wu

, Md Rakibul Hasan

2 a

and Md Zakir Hossain

3,4 b

School of Computing, Australian National University, Canberra ACT 2600, Australia

Department of Electrical and Electronic Engineering, BRAC University, Dhaka 1212, Bangladesh

CSIRO Agriculture & Food, Black Mountain, Canberra ACT 2600, Australia

Biological Data Science Institute, School of Biology, Australian National University, Canberra ACT 2600, Australia

Keywords:

Neural Network, Evolutionary Algorithm, Neural Network Pruning, Anger Veracity, Pupillary Response.

Abstract:

Future human-computing research could be enhanced by recognizing attitude/emotion (for example, anger)

from observers’ reactions (for example, pupillary responses). This paper analyzes observers’ pupillary re-

sponses by developing neural network (NN) models to distinguish between genuine and posed anger. Any

model’s relatively high classiﬁcation accuracy means the pupillary responses and observed anger (genuine

or posed) are deeply connected. In this connection, we implemented strategies for tuning parameters of the

model, methods to optimize and compress the model structure, analyze the similarity of hidden units, and

decide which of them should be removed. We achieved the goal of removing the network’s redundant neurons

without signiﬁcant performance decline and improved the training speed. Finally, our evolutionary-based NN

model showed the highest accuracy of 86% with a 3-layers structure and outperformed the backpropagation-

based NN. The high accuracy highlights the potential of our model to use in the future for distinguishing

observers’ reactions to emotion/attitude recognition.

1 INTRODUCTION

With the increase of technology utilization like smart-

phones and the Internet, computing appears not only

as traditional desktop computers but also in myriad

applications for the betterment of humans, such as

smart classrooms, smart sensing wearables for health-

care, wellness, and sports. Research in human com-

puting is emerging with a particular interest in rec-

ognizing, processing, and responding to users’ non-

verbal cues like emotions (Lim et al., 2020). Users’

emotions could be estimated from various reactions

such as pupillary response, facial expressions (Hos-

sain and Gedeon, 2017), and speech (Han et al.,

2014).

Anger is one of the strongest emotions, and

this characteristic makes anger easily felt by others

(Kannis-Dymand et al., 2019). However, anger seems

to be easily acted out. People are generally poor at

distinguishing genuine and acted anger, and their ac-

curacy is only around 65% (Qin et al., 2018). Au-

https://orcid.org/0000-0003-2565-5321

https://orcid.org/0000-0003-1892-831X

thors in (Qin et al., 2018) classiﬁed genuine and acted

anger using pupillary dilation signals in a combina-

tion of NNs and crowd prediction techniques, through

which they introduced their Misaka networks (collec-

tion of multiple NNs, and later their aggregation for

the ﬁnal outcome). (Jin et al., 2020) compared anger

veracity classiﬁcation performance between a fully-

connected NN and a long-short term memory net-

work utilizing observers’ pupillary responses. They

further applied an outlier detection technique to im-

prove performance. Their NN model employed two

hidden layers consisting of 20 and 10 hidden units,

respectively. Further, they used a dropout layer and

a batch normalization technique to prevent overﬁt-

ting. With a genetic-based feature selection method to

select observer’s pupillary response features, (Huang

et al., 2020) showed a performance improvement of

a two-stream NN model having 60 hidden neurons in

each sub-stream, followed by another layer for ﬁnal

prediction. Few works (Qin et al., 2018; Jin et al.,

2020; Huang et al., 2020) focused on improving per-

formance by incorporating more computational bur-

dens, such as more hidden units, layers, etc. Our

contribution is to ﬁnd out the simplest model possi-

Wu, F., Hasan, M. and Hossain, M.

An Evolutionary-based Neural Network for Distinguishing between Genuine and Posed Anger from Observers’ Pupillary Responses.

DOI: 10.5220/0010985100003116

In Proceedings of the 14th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2022) - Volume 2, pages 653-661

ISBN: 978-989-758-547-0; ISSN: 2184-433X

653

ble along with preserving a reasonable performance

score. We have two speciﬁc novel contributions com-

pared to the previous researches above—we further

prune our developed simple NN model by removing

redundant neurons, and we utilize evolutionary algo-

rithm (EA) training.

Other than anger, there are several works on

other emotion / facial expression recognition as well.

For example, (Hossain and Gedeon, 2017) classiﬁed

posed and genuine smiles from observers’ periph-

eral physiology (blood volume pulse, galvanic skin

response, and pupillary response), and (Irani et al.,

2016) recognized human stress using their facial im-

ages. Furthermore, authors in (Han et al., 2014) em-

ployed NN to estimate emotional state from speech

signals.

In this current paper’s scenario, instead of using

volunteers’ subjective judgment, we use their pupil-

lary responses as an objective discriminating result.

We utilize a set of data from (Chen et al., 2017)

containing information about perceivers’ pupillary re-

sponse when they were watching genuine or posed

anger videos. Consistently, we build up a NN model

to train on the collected data. If the model has a good

performance, we can say that perceivers can discrim-

inate between the two kinds of anger physiologically

and unconsciously. Also, we implement some net-

work optimization and reduction methods according

to (Wang, 2018) for improving our NN as good as

possible. This good result helps prove people’s ability

to discriminate posed and acted anger physiologically.

Besides using a usual backpropagation method to

train the NN, we also implement an EA to optimize

the network. The basic idea of using EA is that the tra-

ditional method gives us a model that does not reach

very high accuracy. We want to ﬁnd whether the per-

formance problem can be attributed to the learning

being constrained to a local minimum or the best per-

formance of the NN model on this dataset has been

reached. Since (Korolev, 2010) showed that EA, as a

general method, can have a good effect on multiple-

minimum problems, we decided to implement EA.

Figure 1 depicts the steps involved in this work.

An EA cannot be used on a classiﬁcation problem

directly. For a NN with a fully-known structure, we

can generally regard the collection of all weights and

biases in the network as an individual, which means

every individual in the population corresponds to one

NN with that same structure. After that, we can apply

an EA to optimize the individual (NN).

After we train the model, we also want to optimize

its structure. The best structure of a NN usually can-

not be decided at ﬁrst (Kowalski and Kusy, 2018). We

can initialize a NN with more extra neurons and then

reduce its redundancy (Tung and Mori, 2020). Re-

ducing the redundancies is beneﬁcial, but this is difﬁ-

cult to decide which neuron should be removed. One

implemented strategy in (Wang, 2018) is to analyze

the output from the target hidden layer of the NN and

use the analysis result to decide which neurons to be

deleted.

2 METHOD

The outline of the technique implemented on the

dataset in this paper is divided into three parts. They

are explained as follows.

i. Developing a NN prediction model considering

observers’ pupillary responses: The network is

implemented with three layers of neurons with

basic techniques, including backpropagation and

cross-entropy loss function.

ii. Incorporating EA as a substitutional method to

optimize the NN model: We apply EA opti-

mization instead of backpropagation training and

compare the performance between EA and back-

propagation.

iii. Applying an appropriate pruning technique in the

NN model for data compression and reduction

(Gedeon and Harris, 1992; Wang, 2018): We

systematically reduce the number of hidden units

from our developed NN model to develop a sim-

ple yet effective model.

2.1 Classiﬁcation of Genuine or Posed

Anger

The Anger dataset is collected from the literature

(Chen et al., 2017). Before describing speciﬁc meth-

ods to accomplish the classiﬁcation task, a detailed

inspection of the dataset is given in the following sub-

section.

2.1.1 Data Inspection

The dataset is utilized to ﬁgure out the relationship

between humans’ unconscious physiological charac-

teristics and the observed anger videos. The experi-

ment where the data comes from asks 20 volunteers to

watch 20 videos. Among the 20 videos, there are 10

videos with genuine anger and 10 videos with posed

anger. All the videos came from YouTube, and the ex-

periment designer made the videos of genuine anger

from documentaries and news, while the posed anger

videos were made from movies. All other factors that

may potentially inﬂuence the volunteers’ judgment

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654

Genuine

Posed

Anger

videos

Observer

Pupillary

response

Neural

network

classifier

Back propagation

Optimization

Evolutionary Algorithm

Optimization

Trained

Network

Hidden

layer

Compres

sion

Target

Neural

network

Figure 1: Flowchart to obtain the target neural network architecture either via backpropagation optimizing or evolutionary

algorithm optimizing.

are removed as much as possible. For example, the

resolution of the videos was kept the same, and the

actors in the posed anger movies were not famous.

Therefore, the possibility of volunteers knowing the

actor and perceiving the acted anger in the movie was

reduced. Now, the dataset may seem small. However,

a similar work on genuine smile detection from ob-

servers’ physiological states also collected data from

20 observers watching 19 video samples (Hossain and

Gedeon, 2019). Furthermore, they reported accuracy

with increasing the number of observers and the num-

ber of training videos. Their research reveals that the

accuracy did not improve much at around 16–19 ob-

servers, and there was no increase in accuracy at all

after 14 videos. Therefore, around 16 observers and

14 videos appeared as the minimum number of ob-

servers and video samples required for satisfactory

performance. Thus, our dataset size having 20 ob-

servers and 20 videos is reasonable.

Six features/dimensions were extracted from the

dataset. The Mean and the Std refer to the mean

and standard deviation of observers’ pupillary re-

sponse. Diff1 and Diff2 refer to the change of ob-

server’s left and right pupil size, respectively. Fur-

thermore, PCAd1 and PCAd2 denote the orthogonal

linear transformation with the ﬁrst and second princi-

pal components, respectively.

Figure 2 illustrates the density plots of all six di-

mensions from where we can ﬁnd some clues, such

as different dimensions of data have different distri-

butions when they belong to genuine or posed la-

bels. Five dimensions out of six (except PCAd1) seem

to have similar density plots of the genuine and the

posed labels, which means they may contribute less

to the classiﬁcation. Only input dimension PCAd1

shows noticeable differences between the two kinds

of data. To solve this kind of classiﬁcation problem,

there are some common choices, including k-nearest

neighbor (KNN), support vector machine (SVM), and

NN according to (Hossain and Gedeon, 2017). How-

ever, for this dataset, the difference showed in PCAd1

may not support the KNN and SVM to perform well

in classifying the data. Compared with SVM and

KNN, authors of (Hossain and Gedeon, 2017) also

mentioned that the ﬁnal accuracy result with the NN

was the best on their smile dataset. Thus, we decided

to build a NN for this anger classiﬁcation problem.

Accordingly, our developed NN model represents the

genuine and posed labels as 0 and 1, respectively.

2.1.2 Data Preprocessing

Table 1 reports the average and standard deviation of

the six dimension input patterns, which needs pre-

processing. Otherwise, the learning process will be

signiﬁcantly degraded since the Mean dimension can

have big weights when the training is in an early

phase.

Table 1: The average and standard deviation of six dimen-

sions of input data.

Dimension Average Standard Deviation

Mean 0.88909015 0.04603393

Std 0.10246244 0.06934124

Diff1 0.00842139 0.0065422

Diff2 0.20957463 0.08669128

PCAd1 0.03070341 0.01101771

PCAd2 0.12138183 0.0235699

Suitable data preprocessing can improve the per-

formance of data-driven models (Tang et al., 2020).

We conduct data preprocessing according to Equa-

tion (1).

x − x

mean

(1)

where, x is the raw data before preprocessing, x

mean

and σ

are the mean and standard deviation of x, re-

spectively. The standardized data x

was considered

as input in the NN model.

One of the beneﬁts of data preprocessing is speed-

ing up the training process. The Anger dataset takes

around 20000 epochs to converge without data pre-

processing with the same NN model, optimizer, and

loss function. On the other hand, it only takes 5000

An Evolutionary-based Neural Network for Distinguishing between Genuine and Posed Anger from Observers’ Pupillary Responses

655

(a) PCAd1. (b) PCAd2. (c) Diff1.

(d) Mean. (e) Diff2. (f) Std.

Figure 2: Density plots for different input dimensions of genuine and posed labels.

epochs to converge after the above preprocessing.

One interesting thing is that although the learning rate

is the same for both cases, the reduction of the train-

ing loss for the model without data preprocessing is

very little in the ﬁrst 5000 epochs. After 5000 epochs

of training, the reduction comes to a relatively nor-

mal speed. This phenomenon implies that the model

may consume much time in adapting to the unbal-

anced data.

2.1.3 Basic Neural Network Model

Except for input neurons, the built network has three

layers of neurons with 6, 6, and 1 neuron, respec-

tively. At ﬁrst, the number of hidden neurons was

set to 10; however, it was reduced through systematic

performance tests.

The ﬁrst and second hidden layers use activa-

tion function ReLU, and the last layer has activa-

tion function Sigmoid for classiﬁcation purposes. The

loss function is binary cross-entropy which ﬁts binary

classiﬁcation problems along with Sigmoid function.

The optimization method is stochastic gradient de-

scent (SGD).

2.2 Evolutionary Algorithm on

Optimizing Neural Network

2.2.1 Coding of Individuals

The target NN, as mentioned above, has four layers

with an input of six units, two hidden layers of six

units each, and an output layer of one unit. Therefore,

to fully store weights and biases of the network, each

individual in the population needs to have a length

according to Equation (2).

L = Units

input

×Units

hidden1

+Units

hidden1

+Units

hidden1

×Units

hidden2

+Units

hidden2

+Units

hidden2

×Units

out put

+Units

out put

(2)

Since the network is fully connected, there are

(6 × 6) weights between the input layer and second

layer (i.e., ﬁrst hidden layer), (6 × 6) weights be-

tween the second and third layers, and (6×1) weights

between the third and last layer ((i.e., output layer).

Additionally, we have (6 + 6 + 1) biases to store.

Therefore, the length of the individual code should be

91, consisting of all weights and biases. Individuals

should have a form according to Equation (3).



, . . . , x



i = 1, 2, . . . , N (3)

where, x

= real numbers of weights or bias, t = gen-

eration of the individual, i = number of individuals,

n = number of genes (91), and N = number of popu-

lations.

2.2.2 Evaluation Function (Decoding of

Individuals)

To evaluate an individual in the population, we ex-

tract real numbers from it and assign the real num-

bers’ value to NN parameters.

After assigning the values, we have the new net-

work with weights and biases optimized by the EA.

Then we apply the evaluate accuracy method, which

is used in the backpropagation optimization to get the

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656

accuracy on the whole dataset. Then we take it as the

ﬁtness of this particular individual. Accordingly, we

need to maximize the ﬁtness function during the pro-

cess of EA.

2.2.3 Population Initialization

Every individual is initialized by a random number

generator that generates 91 real numbers with stan-

dard normal distribution. As such, we generate 100

individuals as the initial population.

2.2.4 Selection

We use tournament selection to select the best indi-

viduals from the total population, where the tournsize

parameter is set to three. Therefore, every time, we

randomly pick up three individuals from the popula-

tion and choose the individual with the best ﬁtness to

the pool of offspring. The procedure will repeat until

the required number of individuals has been selected.

2.2.5 Crossover

We have used blend crossover to realize gene transfer

in the population.

2.2.6 Mutation

For every gene (real number) in the mutated individ-

ual, there will be an independent probability that this

gene will change to a random number selected ac-

cording to the Gaussian distribution with the mean

value unchanged. The variance of the Gaussian dis-

tribution is set to 0.1, and the independent probability

is 0.4.

2.2.7 General Settings

For the EA, we initialize the population as 100 indi-

viduals. The crossover probability is 0.8, the muta-

tion probability is 0.4, and we set the algorithm will

run for 800 generations.

2.3 Compression on Hidden Layer

After implementing a NN classifying genuine and

posed anger and using an EA to optimize it, we want

to address the problem that if the 6, 6, 1 layer structure

of the network has some redundancies. So, we con-

duct the following analysis and techniques to reduce

the hidden layer units of the trained NN.

2.3.1 Distinctiveness Analysis

This analysis is towards the activation matrix, which

is the output matrix of a particular layer of neurons

when all training input enters the model. Every col-

umn of the matrix is a vector that corresponds to a

neuron. Thus, we can analyze the distinctiveness be-

tween units according to these vectors. The steps im-

plementing this analysis are shown below:

i. We calculate the activation matrix for the hidden

units in the layer to be analyzed, which is the sec-

ond hidden layer.

ii. We know that every column in the activation

matrix corresponds to one hidden unit’s output.

Thus, by analyzing the similarities between these

columns, we can get the distinctiveness of hidden

units. We use cosine similarity (Equation (4)) to

measure distinctiveness.

cos similarity =

·V

k · kV

(4)

where, V

and V

are the i

and j

column vectors

of the activation matrix.

iii. For every two hidden units in the second hid-

den layer, we analyze their similarities. Then we

record the most similar two units’ index i and j.

2.3.2 Pruning Network

After ﬁnding two similar hidden units, we deleted the

ﬁrst unit and added its weights and bias to the second

unit. We delete one of the most similar two hidden

units in the second hidden layer whenever we prune

the network. Then we ﬁne-tune the network with

1000 epochs using backpropagation training.

3 RESULT AND DISCUSSION

3.1 Neural Network and Its Basic

Optimization

At ﬁrst, the model is trained with not-preprocessed

data. After training several times with different learn-

ing rates, and ﬁnally at 0.05 with a momentum of 0.8,

the best test accuracy is not higher than 55% as shown

in Figure 3a. First useful optimization is to change the

activation function from ReLU to tanh. After using

tanh as the ﬁrst two layers’ activation function, test

accuracy can reach around 58% as Figure 3b depicts.

Then the data is preprocessed, and thus the speed

of learning is found much higher. After using differ-

ent activation functions, ReLU retrieves the best func-

tion position. With preprocessed data, ReLU function,

An Evolutionary-based Neural Network for Distinguishing between Genuine and Posed Anger from Observers’ Pupillary Responses

657

(a) ReLU.

(b) tanh.

Figure 3: Test accuracy with not-preprocessed data at two

different activation functions.

and learning rate of 0.001, the model can achieve the

best test accuracy of around 75% (Figure 4a). How-

ever, tanh does not have a good performance in this

case. After preprocessing the data, one of the most

important factors of the model which we can tune

is the learning rate, and the ﬁnal progress is made

by raising this learning rate. When the learning rate

is higher than 0.01 (0.017 in Figure 4b) with a mo-

mentum of 0.9, the best test accuracy can reach 85%

shown in Figure 4b.

3.2 Evolutionary Algorithm for

Optimization

The design of EA for optimizing the NN is presented

previously in Section 2.2. The accuracy with that EA

optimization is shown in Figure 5.

Since we randomly initialize all weights and bi-

ases of the network, the mean accuracy of individuals

at initial generation is 50%. Then with the progress

of evolution, the best accuracy raises to around 86%.

For this optimization method, we can use the best in-

dividual in the last generation of the population as the

ﬁnal weights and biases for our NN. Therefore, we

can conclude that the EA has trained the model to an

accuracy of 86%.

(a) Learning rate = 0.001.

(b) Learning rate = 0.017.

Figure 4: Test accuracy with preprocessed data and ReLU

activation function at two different learning rates.

3.3 Neural Network Pruning

While implementing the hidden-layer pruning pro-

cess, we investigate the inﬂuence of pruning hidden

units on the model’s performance. To demonstrate the

pruning process, Figure 6a to 6d shows the accuracy

results of the network after pruning one, two, three,

and four hidden units, respectively. Without pruning,

the network is ﬁrst trained 10000 epochs of backprop-

agation. Then every time, we prune one hidden layer

unit from the network and train another 1000 epochs.

According to the pruning result, although the net-

work with six hidden units can learn well, there is

still space to optimize the model since some hidden

units are redundant. When we reduce the number

of hidden units to three, the model’s performance re-

mains around 90% of its peak. However, when we

deleted the fourth unit, the performance declined sig-

niﬁcantly. Therefore, we can conclude that this model

may need at least three hidden units in the hidden lay-

ers to learn from data. This analysis supports the con-

clusion in (Gedeon and Harris, 1992) that when we

want to compress an image with a large scaler, the

quality will decline because the remaining units in the

hidden layer are not enough to store the majority of

information of the image.

ICAART 2022 - 14th International Conference on Agents and Artiﬁcial Intelligence

658

Figure 5: Accuracy of the neural network model optimized by evolutionary algorithm.

(a) One unit pruned. (b) Two units pruned.

Figure 6: Accuracies with the pruning process on the hidden layer. Each time, one hidden unit is removed, and the model is

trained for another 1000 epochs of backpropagation.

4 COMPARISON AND

LIMITATIONS

Firstly, we can compare the backpropagation training

method and EA results on the NN. After tuning sev-

eral hyperparameters of the backpropagation method,

the model after 10000 epochs of training can reach an

accuracy of 85%. On the contrary, the EA achieves an

accuracy of 86% with 800 generations of evolution.

These two similar accuracies can address the prob-

lem we mentioned in the Introduction section: can the

backpropagation method obtain 85% accuracy due to

being constrained by some local minimum? We can-

not conclude with complete certainty, but the answer

can be ‘no’ because EA usually has a good perfor-

mance on ﬁnding the global best solution, and its best

accuracy is still around 85%.

Table 2 reports a performance comparison among

NN-based anger veracity recognition using the same

Anger dataset we utilized in this study.

The table reveals that (Huang et al., 2020) re-

ported around 93.6% accuracy. They achieved this

An Evolutionary-based Neural Network for Distinguishing between Genuine and Posed Anger from Observers’ Pupillary Responses

659

Table 2: Performance comparison among similar NN-based

works that utilized the same Anger dataset.

Reference Accuracy

(Huang et al., 2020) 93.6%

Proposed 86.0%

(Qin et al., 2018) 83.3%

(Jin et al., 2020) 79.7%

score using a two-stream NN-based model, where

each sub-stream has 60 hidden neurons followed by

a single fully-connected ﬁnal prediction layer. It is

worth noticing that we used a simpler feedforward

single stream architecture with only twelve hidden

units, which is at least ten times less than what they

used. We aimed to achieve reasonable accuracy using

minimum computational burden (hidden units). We

fulﬁll our aim by accomplishing a reasonably good

performance (86%) with a simple structure after tun-

ing the hyperparameter and hidden layer compres-

sion.

5 CONCLUSION AND FUTURE

WORK

Anger is one of the many powerful emotions of hu-

mans, and ﬁnding its authenticity is essential in emo-

tion recognition and human-centered computing ar-

eas. We have developed a simple NN to classify real

and posed anger based on observers’ pupillary re-

sponses. Our research indicates that the anger classi-

ﬁcation is achievable by employing this simple struc-

tured NN. We have optimized the NN in two ways,

namely backpropagation and EA. With some rea-

sonable adjustment during testing the network, we

have developed a model that provided as high as

86% accuracy. This high accuracy proved that per-

ceivers’ pupillary response patterns could reﬂect the

anger they saw as genuine or posed even though the

perceivers are unconscious. Furthermore, our study

shows that three neurons can be removed without sig-

niﬁcant performance degradation through NN prun-

ing.

To ensure the superiority of our proposed ap-

proach, future works will include alternative mod-

els to benchmark and various statistical tests, for ex-

ample Augmented Dickey-Fuller test, Kolmogorov-

Smirnova test, and Shapiro-Wilk test (Fan et al.,

2021) to validate. To build a relationship between

emotion and speech, authors in (Han et al., 2014) have

developed an efﬁcient single-hidden-layer NN (called

extreme learning machine) for emotion recognition

based on utterance level speech features. Therefore,

for future work, we shall design and optimize a net-

work that uses people’s speech emotion to predict

whether their facial emotion is genuine or not, which

could be interesting. Moreover, we can also imple-

ment advanced network pruning techniques, such as

using thresholds and structured ﬁlter level pruning

(Luo et al., 2017).

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