When automatic facial expression recognition is applied to video sequences of speaking subjects, the recognition accuracy has been noted to be lower than with video sequences of still subjects. This effect known as the speaking effect arises during spontaneous conversations, and along with the affective expressions the speech articulation process influences facial configurations. In this work we question whether, aside from facial features, other cues relating to the articulation process would increase emotion recognition accuracy when added in input to a deep neural network model. We develop two neural networks that classify facial expressions in speaking subjects from the RAVDESS dataset, a spatio-temporal CNN and a GRU cell RNN. They are first trained on facial features only, and afterwards both on facial features and articulation related cues extracted from a model trained for lip reading, while varying the number of consecutive frames provided in input as well. We show that using DNNs the addition of features related to articulation increases classification accuracy up to 12%, the increase being greater with more consecutive frames provided in input to the model.

Improving the accuracy of automatic facial expression recognition in speaking subjects with deep learning / S. Bursic, G. Boccignone, A. Ferrara, A. D'Amelio, R. Lanzarotti. - In: APPLIED SCIENCES. - ISSN 2076-3417. - 10:11(2020 Jun), pp. 4002.1-4002.15. [10.3390/app10114002]

Improving the accuracy of automatic facial expression recognition in speaking subjects with deep learning

S. Bursic
;
G. Boccignone;A. Ferrara;A. D'Amelio;R. Lanzarotti
2020-06

Abstract

When automatic facial expression recognition is applied to video sequences of speaking subjects, the recognition accuracy has been noted to be lower than with video sequences of still subjects. This effect known as the speaking effect arises during spontaneous conversations, and along with the affective expressions the speech articulation process influences facial configurations. In this work we question whether, aside from facial features, other cues relating to the articulation process would increase emotion recognition accuracy when added in input to a deep neural network model. We develop two neural networks that classify facial expressions in speaking subjects from the RAVDESS dataset, a spatio-temporal CNN and a GRU cell RNN. They are first trained on facial features only, and afterwards both on facial features and articulation related cues extracted from a model trained for lip reading, while varying the number of consecutive frames provided in input as well. We show that using DNNs the addition of features related to articulation increases classification accuracy up to 12%, the increase being greater with more consecutive frames provided in input to the model.
Affective computing; Deep learning; Emotion recognition; Facial expression recognition; Speaking effect
Settore INF/01 - Informatica
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
Stairway to elders: bridging space, time and emotions in their social environment for wellbeing
Article (author)
File in questo prodotto:
File Dimensione Formato  
applsci-10-04002.pdf

accesso aperto

Tipologia: Publisher's version/PDF
Dimensione 10.42 MB
Formato Adobe PDF
10.42 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/747320
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 9
  • ???jsp.display-item.citation.isi??? 5
social impact