Real-time facial expression recognition using smoothed deep neural network ensemble
Year: 2020
Type: article
Abstract: Facial emotion recognition (FER) has been extensively researched over the past two decades due to its direct impact in the computer vision and affective robotics fields. However, the available datasets to train these models include often miss-labelled data due to the labellers bias that drives the model to learn incorrect features. In this paper, a facial emotion recognition system is proposed, addressing automatic face detection and facial expression recognition separately, the latter is performed by a set of only four deep convolutional neural network respect to an ensembling approach, while a label smoothing technique is applied to deal with the miss-labelled training data. The proposed system takes only 13.48 ms using a dedicated graphics processing unit (GPU) and 141.97 ms using a CPU to recognize facial emotions and reaches the current state-of-the-art performances regarding the challenging databases, FER2013, SFEW 2.0, and ExpW, giving recognition accuracies of 72.72%, 51.97%, and 71.82% respectively.
DOI: 10.3233/ica-200643
Authors: Nadir Kamel Benamara, Mikel Val-Calvo, Jose Ramón Álvarez-Sánchez, Alejandro Díaz-Morcillo, Jose Manuel Ferrández-Vicente, Eduardo Fernández-Jover, Tarik Boudghene Stambouli
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W3082932313