Photogram Classification-Based Emotion Recognition

Juan Miguel López-Gil, Nestor Garay-Vitoria

2021 - IEEE Access Vol. 9

Artículo en revista

Línea investigación:
Computación Emocional
Autores (p.o. de firma):
Juan Miguel López-Gil, Nestor Garay-Vitoria

This paper presents a method for facial emotion recognition based on parameterized photograms and machine learning techniques. Videos of people displaying emotions are parameterized by a facial feature-based emotional category association process to determine whether a given photogram expresses emotions by comparing the facial action units displayed with findings in the literature about facial emotion. To test the proposed approach, two strategies are adopted. First, photograms displaying emotions are gathered, and then different machine learning classifiers are applied to check the goodness of the obtained set of categorized emotional photograms. Second, classifiers trained on the sets of emotional photograms were then used to emotionally classify all the videos in each database, using all the photograms with no preprocessing or photogram selection. The presented method was tested using the OpenFace parameterizer with emotional videos gathered from Multimedia Understanding Facial Expression (MUG) and Cohn-Kanade (CK+) databases. The outcomes achieved for emotional photogram classification on the sets of emotional photograms reached maximums of 99.80% and 99.63% in the MUG and CK+ databases, respectively. The videos were classified using different voting strategies regarding the outcome of each photogram in the video with all the photogram emotion recognition classifiers obtained results reflecting recognition rates of 70.71% and 66.36% for the videos in MUG and CK+ databases, and reached up to 72.55% and 88.37% when classifier combination strategies were used. The work carried out opens the door to follow-up work concerning data preprocessing and the use of different classifier combination methods in facial emotion recognition.

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Lugar publicación:
IEEE Access
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Indicadores calidad:
JCR Science Edition (2020): IF=3,367 (Q2 - Computer Science, Information Systems)
SJR SCOPUS (2020): IF=0,587 (Q1 - Computer Science, Miscellaneous)
Nombre publicación:
IEEE Access Vol. 9