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


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 (, AffectNet ( and SEWA ( 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 (

Code availability

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


  1. 1.

    Posner, J., Russell, J. A. & Peterson, B. S. The circumplex model of affect: an integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev. Psychopathol. 17, 715–734 (2005).

    Article  Google Scholar 

  2. 2.

    Russell, J. A circumplex model of affect. J. Pers. Soc. Psychol. 39, 1161–1178 (1980).

    Article  Google Scholar 

  3. 3.

    Gunes, H. & Schuller, B. Categorical and dimensional affect analysis in continuous input: current trends and future directions. Image Vision Comput. 31, 120–136 (2013).

    Article  Google Scholar 

  4. 4.

    Sariyanidi, E., Gunes, H. & Cavallaro, A. Automatic analysis of facial affect: a survey of registration, representation, and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37, 1113–1133 (2015).

    Article  Google Scholar 

  5. 5.

    Mollahosseini, A., Hasani, B. & Mahoor, M. H. AffectNet: a database for facial expression, valence, and arousal computing in the wild. IEEE Trans. Affect. Comput. 10, 18–31 (2019).

    Article  Google Scholar 

  6. 6.

    Kossaifi, J., Tzimiropoulos, G., Todorovic, S. & Pantic, M. AFEW-VA database for valence and arousal estimation in-the-wild. Image Vision Comput. 65, 23–36 (2017).

    Article  Google Scholar 

  7. 7.

    Kossaifi, J. et al. SEWA DB: a rich database for audio-visual emotion and sentiment research in the wild. IEEE Trans. Pattern Anal. Mach. Intell. (2019).

  8. 8.

    He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 770–778 (2016).

  9. 9.

    Lin, L. I.-K. A concordance correlation coefficient to evaluate reproducibility. Biometrics 45, 255–268 (1989).

    Article  Google Scholar 

  10. 10.

    Ringeval, F. et al. AV+ EC 2015: The first affect recognition challenge bridging across audio, video, and physiological data. In Proc. 5th International Workshop on Audio/Visual Emotion Challenge (ACM, 2015).

  11. 11.

    Valstar, M. et al. AVEC 2016: Depression, mood, and emotion recognition workshop and challenge. In Proc. 6th International Workshop on Audio/Visual Emotion Challenge (ACM, 2016).

  12. 12.

    Ringeval, F. et al. Avec 2017: Real-life depression, and affect recognition workshop and challenge. In Proc. 7th Annual Workshop on Audio/Visual Emotion Challenge (ACM, 2017).

  13. 13.

    Ringeval, F. et al. Avec 2018 workshop and challenge: Bipolar disorder and cross-cultural affect recognition. In Proc. 2018 on Audio/Visual Emotion Challenge and Workshop (ACM, 2018).

  14. 14.

    Kollias, D., Cheng, S., Ververas, E., Kotsia, I. & Zafeiriou, S. Deep neural network augmentation: generating faces for affect analysis. Int. J. Comp. Vision 128, 1455–1484 (2020).

    Article  Google Scholar 

  15. 15.

    Jang, Y., Gunes, H. & Patras, I. Registration-free face-ssd: Single shot analysis of smiles, facial attributes, and affect in the wild. Comput. Vision Image Understanding 182, 17–29 (2019).

  16. 16.

    Bulat, A. & Tzimiropoulos, G. How far are we from solving the 2D & 3D face alignment problem? (And a dataset of 230,000 3D facial landmarks). In Proc. IEEE International Conference on Computer Vision (2017).

  17. 17.

    Vaswani, A. et al. Attention is all you need. In Advances in Neural Information Processing Systems 5998–6008 (2017).

  18. 18.

    Hinton, G., Vinyals, O. & Dean, J. Distilling the knowledge in a neural network. In NIPS Deep Learning and Representation Learning Workshop (NIPS, 2015).

  19. 19.

    Furlanello, T., Lipton, Z., Tschannen, M., Itti, L. & Anandkumar, A. Born-again neural networks. In International Conference on Machine Learning 1602–1611 (2018).

  20. 20.

    Mitenkova, A., Kossaifi, J., Panagakis, Y. & Pantic, M. Valence and arousal estimation in-the-wild with tensor methods. In 14th IEEE International Conference on Automatic Face & Gesture Recognition (2019).

  21. 21.

    Kollias, D. et al. Deep affect prediction in-the-wild: Aff-Wild database and challenge, deep architectures, and beyond. Int. J. Comput. Vision 127, 907–929 (2019).

    Article  Google Scholar 

  22. 22.

    Russell, J., J.A, B. & Fernandez-Dols, J. Facial and vocal expressions of emotions. Annu. Rev. Psychol. 54, 329–349 (2003).

    Article  Google Scholar 

  23. 23.

    Grimm, M. & Kroschel, K. in Robust Speech (eds Grimm, M. & Kroschel, K.) Ch. 16 (IntechOpen, 2007).

  24. 24.

    Marsh, A. A., Kozak, M. N. & Ambady, N. Accurate identification of fear facial expressions predicts prosocial behavior. Emotion 7, 239–251 (2007).

    Article  Google Scholar 

  25. 25.

    Clark, C. M., Gosselin, F. & Goghari, V. M. Aberrant patterns of visual facial information usage in schizophrenia. J. Abnorm. Psychol. 122, 513–519 (2013).

    Article  Google Scholar 

  26. 26.

    Kring, A. M. & Elis, O. Emotion deficits in people with schizophrenia. Annu. Rev. Clin. Psychol. 9, 409–433 (2013).

    Article  Google Scholar 

  27. 27.

    Bishay, M., Palasek, P., Priebe, S. & Patras, I. Schinet: Automatic estimation of symptoms of schizophrenia from facial behaviour analysis. IEEE Trans. Affective Comput. (2019).

  28. 28.

    Caligiuri, M. P. & Ellwanger, J. Motor and cognitive aspects of motor retardation in depression. J. Affective Disord. 57, 83–93 (2000).

    Article  Google Scholar 

  29. 29.

    Dibeklioğlu, H., Hammal, Z. & Cohn, J. F. Dynamic multimodal measurement of depression severity using deep autoencoding. IEEE J. Biomed. Health Inform. 22, 525–536 (2017).

    Article  Google Scholar 

  30. 30.

    Harms, M. B., Martin, A. & Wallace, G. L. Facial emotion recognition in autism spectrum disorders: a review of behavioral and neuroimaging studies. Neuropsychol. Rev. 20, 290–322 (2010).

    Article  Google Scholar 

  31. 31.

    Rudovic, O., Lee, J., Dai, M., Schuller, B. & Picard, R. W. Personalized machine learning for robot perception of affect and engagement in autism therapy. Sci. Robot. 3, eaao6760 (2018).

    Article  Google Scholar 

  32. 32.

    Boy, N., Lidén, K. & Jacobsen, E. K. Societal Ethics of Biometric Technologies (SOURCE Societal Security Network, 2018).

  33. 33.

    Cowie, R. in The Oxford Handbook of Affective Computing 334–348 (Oxford Univ. Press, 2015).

  34. 34.

    Gendron, M., Crivelli, C. & Barrett, L. F. Universality reconsidered: diversity in making meaning of facial expressions. Curr. Directions Psychol. Sci. 27, 211–219 (2018).

    Article  Google Scholar 

  35. 35.

    Bryant, D. & Howard, A. A comparative analysis of emotion-detecting ai systems with respect to algorithm performance and dataset diversity. In Proc. 2019 AAAI/ACM Conference on AI, Ethics, and Society 377–382 (2019).

  36. 36.

    Rhue, L. Racial influence on automated perceptions of emotions. Preprint at (2018).

  37. 37.

    Pantic, M. & Bartlett, M. S. in Face recognition (eds Delac, K. & Grgic, M.) Ch. 21 (IntechOpen, 2007).

  38. 38.

    Smith, F. W. & Rossit, S. Identifying and detecting facial expressions of emotion in peripheral vision. PLoS ONE 13, e0197160 (2018).

    Article  Google Scholar 

  39. 39.

    Gastaldi, X. Shake–Shake regularization. Preprint at (2017).

  40. 40.

    Paszke, A. et al. PyTorch: an imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32 (eds Wallach, H. et al.) 8024–8035 (Curran Associates, 2017).

  41. 41.

    Kingma, D. P. & Ba, J. Adam: A method for stochastic optimization. Preprint at (2014).

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Author information




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.

Corresponding author

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).

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