Detecting Anger in Persuasive Spaces: An Evaluation of Facial Expression Algorithms

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.