Cybernetics of the Mind: Learning Individual's Perceptions Autonomously

Abstract

In this paper, we describe an approach to computational modelling and autonomous learning of the perception of sensory inputs by individuals. A hierarchical process of summarization of raw data with heterogeneous nature is proposed. At the lower layer of the hierarchy, the raw data autonomously forms semantically meaningful concepts.Instead of clustering based on the visual or audio similarity, the concepts are being formed at the second layer of the hierarchy based on the observed physiological variables (PVs) like heartbeat, skin conductance, etc. and mapped to the emotional state of the individual. Wearable sensors were used in the experiments. Methodologically, we used the recently introduced Empirical Data Analytics (EDA) computational framework and the data partitioning method within EDA to form the data clouds (cluster-like formations with no pre-defined shape) autonomously and used AnYa type of IF-THEN fuzzy rule-based models to describe the mapping of the observable PVs to the emotional states. Multi-modal typicality distributions, which have properties like pdf and indicate the empirical likelihood distributions of the data, can be further derived without making prior restrictive assumptions traditionally required in statistical approaches. The quality of the classifier is evaluated by a confusion matrix and classification rate/precision. The experimental results are very encouraging: for this extremely complicated problem, we got 74.58% correct classification results without any pre-training.

Enhancing Mouth-Based Emotion Recognition Using Transfer Learning

“…Since scientists do not agree on all the available models, research on ER in any applicative domain starts from the widely recognised model of Ekman, which we chose for our study. Recent research underlines that Ekman's primary emotional states, including happiness, sadness, anger, disgust and neutral [23] can be recognised based on text [24] and physiological clues such as heart rate, skin conductance, gestures, facial expression and sound, which can be managed with a multidimensional approach [11] and compared. Among all ER approaches, facial recognition is still predominant.…”

Enhancing Mouth-based Emotion Recognition using Transfer Learning

“…Since scientists do not agree on all the available models, research on ER in any applicative domain starts from the widely recognized model of Ekman, which we chose for our study. Recent research underlines that Ekman's primary emotional states, including happiness, sadness, anger, disgust, or neutral state [24] can be recognized based on text [25], physiological clues such as heart rate, skin conductance, gestures, facial expression, and sound, that can be managed with a multidimensional approach [8].…”

An Approach for Improving Automatic Mouth Emotion Recognition

“…The process of recognition of moods and sentiments is mostly complex. Recent research underlines that primary emotional states such as happiness, sadness, anger, disgust, or neutral state [38] can be recognized based on text, [31] physiological clues such as heart rate, skin conductance and face expression, differently from sentiment, moods and affect, which are more complex states and be better managed with a multidimensional approach [39] [15]. Since Rosalind Picard defined the challenges for Affective Computing in 2003 [4], numerous advances have been made in the task of emotion recognition, such as defining collective influence of emotions expressed online [9], stating that emotional expressiveness is the crucial fuel that sustains communities; studying cultural aspects of emotions in art [16] and its variations; create emotionally engaging experiences in games [33], where affective changes are crucial to the conscious experience of the world around us.…”