FAMOS: a framework for investigating the use of face features to identify spontaneous emotions

DA COSTA ABREU, Marjory and BEZERRA, G.S. (2019). FAMOS: a framework for investigating the use of face features to identify spontaneous emotions. Pattern Analysis and Applications, 22 (2), 683-701.

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Official URL: https://link.springer.com/article/10.1007/s10044-0...
Link to published version:: https://doi.org/10.1007/s10044-017-0675-y


© 2017, Springer-Verlag London Ltd., part of Springer Nature. Emotion-based analysis has raised a lot of interest, particularly in areas such as forensics, medicine, music, psychology, and human-machine interface. Following this trend, the use of facial analysis (either automatic or human-based) is the most common subject to be investigated once this type of data can easily be collected and is well accepted in the literature as a metric for inference of emotional states. Despite this popularity, due to several constraints found in real-world scenarios (e.g. lightning, complex backgrounds, facial hair and so on), automatically obtaining affective information from face accurately is a very challenging accomplishment. This work presents a framework which aims to analyse emotional experiences through spontaneous facial expressions. The method consists of a new four-dimensional model, called FAMOS, to describe emotional experiences in terms of appraisal, facial expressions, mood, and subjective experiences using a semi-automatic facial expression analyser as ground truth for describing the facial actions. In addition, we present an experiment using a new protocol proposed to obtain spontaneous emotional reactions. The results have suggested that the initial emotional state described by the participants of the experiment was different from that described after the exposure to the eliciting stimulus, thus showing that the used stimuli were capable of inducing the expected emotional states in most individuals. Moreover, our results pointed out that spontaneous facial reactions to emotions are very different from those in prototypic expressions, especially in terms of expressiveness.

Item Type: Article
Uncontrolled Keywords: Facial expression analysis; Face biometrics; Emotion analysis; Action units; Emotion models; 0801 Artificial Intelligence and Image Processing; 0802 Computation Theory and Mathematics; 0906 Electrical and Electronic Engineering; Artificial Intelligence & Image Processing
Identification Number: https://doi.org/10.1007/s10044-017-0675-y
Page Range: 683-701
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 04 Nov 2019 15:03
Last Modified: 18 Mar 2021 03:21
URI: https://shura.shu.ac.uk/id/eprint/25391

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