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.

Building an ecologically valid facial expression database – Behind the scenes / Nonis, F.; Ulrich, L.; Dozio, N.; Antonaci, F. G.; Vezzetti, E.; Ferrise, F.; Marcolin, F.. - ELETTRONICO. - 12768:(2021), pp. 599-616. (Intervento presentato al convegno 15th International Conference on Universal Access in Human-Computer Interaction, UAHCI 2021, held as part of the 23rd International Conference, HCI International 2021) [10.1007/978-3-030-78092-0_42].

Building an ecologically valid facial expression database – Behind the scenes

Nonis F.;Ulrich L.;Antonaci F. G.;Vezzetti E.;Marcolin F.
2021

Abstract

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.
2021
978-3-030-78091-3
978-3-030-78092-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2934812