Modelling on Human Intelligence a Machine Learning System

Pietro, Michela De; Bertacchini, Francesca; Pantano, Pietro; Bilotta, Eleonora

doi:10.1007/978-3-030-39081-5_36

Cited by 1 publication

(2 citation statements)

References 28 publications

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“…The data reported show a wide identification of some emotions (happy, angry and sad) while there is a lower recognition percentage for the others (disgust, fear and surprise). These results are supported by previous studies conducted by the authors [30]. In fact, even if the recognition percentage is lower than the others, the position of a correct label is in any case guaranteed as the result is superior if compared with the percentages assigned to other emotions.…”

Section: Resultssupporting

confidence: 87%

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Modeling and recognition of emotions in manufacturing

Bertacchini

Bilotta

Pietro

et al. 2022

Int J Interact Des Manuf

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New consumer needs have led industries to the possibility of creating virtual platforms where users can customize products by creating infinite combinations of different results. This made it possible to expand sales by guaranteeing a wide choice that would satisfy all requests. The dynamic and flexible evolution of factories is guaranteed by the introduction of new technologies such as robotization and 3D printers, recognized as two of the pillars of Industry 4.0. The main aim of this paper is to achieve a workflow for the creation and implementation of personalised jewellery based on faces with different emotional expressions. To date, there are few works in the literature investigating the intersection between smart manufacturing and emotion recognition, and these are mainly related to improving human–machine interaction. The authors’ aim is to research for innovation in the intersection of three different fields of study such as parametric modelling, smart manufacturing and emotion recognition in order to create personalized and innovative manufacturable models. To this purpose, an application has been generated that exploits both visual scripting, typical of parametric modelling, and scripting, in the Python programming language. The generated algorithm implements a machine learning for emotion recognition that identifies the label of each user-generated face, validating the effectiveness of the method.

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Section: Resultssupporting

confidence: 87%

“…The use of neural networks and of machine learning techniques allow for facial expression analysis [30,44,61]…”

Section: Facial and Emotion Recognitionmentioning

confidence: 99%