Building an ecologically valid facial expression database – behind the scenes
| Autor: | Luca Ulrich, Enrico Vezzetti, Nicolo Dozio, Federica Marcolin, Francesca Giada Antonaci, Francesca Nonis, Francesco Ferrise |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Facial expression
Ecologically-valid data Database Artificial neural network Basic emotions Computer science Emotion classification Context (language use) 3D facial database Affective database Facial expression recognition Human-robot interaction computer.software_genre Convolutional neural network Human–robot interaction Valence (psychology) Set (psychology) computer |
| Zdroj: | Universal Access in Human-Computer Interaction. Design Methods and User Experience ISBN: 9783030780913 HCI (7) |
| Popis: | Artificial Intelligence (AI) algorithms, together with a general increased computational performance, allow nowadays exploring the use of Facial Expression Recognition (FER) as a method of recognizing human emotion through the use of neural networks. The interest in facial emotion and expression recognition in real-life situations is one of the current cutting-edge research challenges. In this context, the creation of an ecologically valid facial expression database is crucial. To this aim, a controlled experiment has been designed, in which thirty-five subjects aged 18–35 were asked to react spontaneously to a set of 48 validated images from two affective databases, IAPS and GAPED. According to the Self-Assessment Manikin, participants were asked to rate images on a 9-points visual scale on valence and arousal. Furthermore, they were asked to select one of the six Ekman’s basic emotions. During the experiment, an RGB-D camera was also used to record spontaneous facial expressions aroused in participants storing both the color and the depth frames to feed a Convolutional Neural Network (CNN) to perform FER. In every case, the prevalent emotion pointed out in the questionnaires matched with the expected emotion. CNN obtained a recognition rate of 75.02%, computed comparing the neural network results with the evaluations given by a human observer. These preliminary results have confirmed that this experimental setting is an effective starting point for building an ecologically valid database. |
| Databáze: | OpenAIRE |
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