Customer satisfaction measuring based on the most significant facial emotion

Mariem Slim (1) , Rostom Kachouri (2) , Ahmed Ben Atitallah (1)

1 LETI - Laboratoire d'électronique et des technologies de l'Information [Sfax]
2 LIGM - Laboratoire d'Informatique Gaspard-Monge

Customer satisfaction (CS) measuring has become one of the strategic tools for companies. Many methods exist to measure customer's satisfaction. However, generally results could be wrong. Otherwise, CS can be deduced from customers emotion state. In this paper, we propose a new end-to-end method for facial emotion detection. For that, six new characteristic features are proposed. We characterize especially the most significant emotions namely " Happy " , " Surprised " and " Neutral ". The proposed method is invariant to camera position. A very challenging datasets as Radboud Faces and Cohn-Kanade (CK+) are used for study and evaluation. Obtained results show that our method reaches a high recognition accuracy and outperforms Action Unit (AU) features based Support Vector Machine (SVM) and K-Nearest Neighbors (KNN).

Mots clés: KNN; —Facial expression; emotion recognition; most significant emotions; feature extraction; action unit; SVM; KNN

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Informatique [cs]; Traitement des images [eess.IV]

Mariem Slim, Rostom Kachouri, Ahmed Ben Atitallah. Customer satisfaction measuring based on the most significant facial emotion. SSD 2018, Mar 2018, Hammamet, Tunisia. pp.502-507, ⟨10.1109/SSD.2018.8570588⟩. ⟨hal-01790317⟩