Detecting Anger in Persuasive Spaces: An Evaluation of Facial Expression Algorithms
Loading...
Date
2020
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
University of Ghana
Abstract
Darwin’s influential work on the recognition of the emotions in man and animals served as the
starting point for emotion research. Based on his work, the basic emotion was theorized, from
which several other emotions have been conceptualised. These emotions are recognised by
both verbal and non-verbal form of communication. Facial expression is the significant and
leading measure for recognition of emotions as 55% of what we communicate is expressed on
our facial expressions. Therefore, facial expression has been applied in diverse fields to detect
emotions such as lie detection and in the medical field for pain analysis; resulting in a plethora
of algorithms or techniques.
Among the negative emotions, anger is said to be the most frequently experienced emotion yet
the most unsatisfactorily handled emotion in both personal and social relations. It is also said
to be the emotion that considerably affects the mental state of an individual. Further, its
intensities like temper, hostility, annoyance, tantrum, agitation, and rage foster harm to the
individual and the surrounding environment as well as have disruptive interpersonal and
intrapersonal consequences.
Currently, anger recognition is performed as part of the multiclass emotion classification or
done using physiological signals or speech data. To the best of our knowledge, there have not
been any studies on how to detect only anger using facial expressions. And even with the
existing approach, there have been some identified issues such as the overlapping of emotion:
anger, fear, and disgust and the difficulty of some of the facial expressions algorithms in
performing a multiclass recognition of emotions. For this reason, we argue that it is key that
the recognition of the anger is done accurately. As, the detection of anger would be useful as it
will provide useful information about peoples’ intensity of anger to manage or control it as
unregulated anger sometimes result in aggression or violence. As such, we want to determine how these facial expression algorithms will perform when used
for binary classification, specifically anger recognition. Therefore, we propose a framework of
three models: SVM (machine learning algorithm), CNN (deep learning algorithm) and a novel
ensemble learning algorithm and PCA as our dimensionality reduction function. The
performance of our models was evaluated on JAFFE, CK+ and KDEF datasets. It was observed
that our proposed models outperformed the state-of-the-art methods, with mention of our novel
ensemble learning model which attained an accuracy of 100% on the JAFFE dataset.Thus we
conclude that the proposed methods are effective for the recognition of anger using facial
expressions and future work will look at evaluating the performance of these algorithms on a
created database of Africans as well as employing these algorithms to detect anger in a
persuasive space and persuade the individual from angry to another emotion for example
happy.
Description
MA. Computer Science
Keywords
anger, persuasive spaces, anger recognition, facial expression, facial expression recognition, facial expression algorithms, facial expression algorithms, machine learning, ensemble learning algorithm,, SVM, CNN