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Estimation of continuous valence and arousal levels from faces in naturalistic conditions

Abstract

Facial affect analysis aims to create new types of human–computer interactions by enabling computers to better understand a person’s emotional state in order to provide ad hoc help and interactions. Since discrete emotional classes (such as anger, happiness, sadness and so on) are not representative of the full spectrum of emotions displayed by humans on a daily basis, psychologists typically rely on dimensional measures, namely valence (how positive the emotional display is) and arousal (how calming or exciting the emotional display looks like). However, while estimating these values from a face is natural for humans, it is extremely difficult for computer-based systems and automatic estimation of valence and arousal in naturalistic conditions is an open problem. Additionally, the subjectivity of these measures makes it hard to obtain good quality data. Here we introduce a novel deep neural network architecture to analyse facial affect in naturalistic conditions with a high level of accuracy. The proposed network integrates face alignment and jointly estimates both categorical and continuous emotions in a single pass, making it suitable for real-time applications. We test our method on three challenging datasets collected in naturalistic conditions and show that our approach outperforms all previous methods. We also discuss caveats regarding the use of this tool, and ethical aspects that must be considered in its application.

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Fig. 1: Valence and arousal circumplex.
Fig. 2: Overview of the traditional approach to facial affect recognition contrasted with our methodology.
Fig. 3: Overview of the architecture of our model.
Fig. 4: Qualitative results of our approach.

Data availability

The datasets analysed during the current study are available from the original authors in the AFEW-VA (https://ibug.doc.ic.ac.uk/resources/afew-va-database/), AffectNet (http://mohammadmahoor.com/affectnet/) and SEWA (https://db.sewaproject.eu/) repositories. The list of cleaned images for the validation and test sets of AffectNet employed in this paper are available on the authors’ Github repository (https://github.com/face-analysis/emonet).

Code availability

The pretrained network, testing code and the annotations of the cleaned AffectNet test and validation sets are available at https://github.com/face-analysis/emonet under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (CC BY-NC-ND).

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Contributions

The code was written by A.T., J.K. and A.B., and the experiments were conducted by A.T. and J.K. The manuscript was written by A.T., J.K., A.B. and M.P.; G.T. helped with discussions regarding the face-alignment network. M.P. supervised the entire project.

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Correspondence to Antoine Toisoul.

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The authors declare no competing interests.

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Supplementary Information

Supplementary text, Figs. 1 and 2, Tables 1 and 2 and refs.

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Toisoul, A., Kossaifi, J., Bulat, A. et al. Estimation of continuous valence and arousal levels from faces in naturalistic conditions. Nat Mach Intell 3, 42–50 (2021). https://doi.org/10.1038/s42256-020-00280-0

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