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Facial Emotion Recognition in-the-Wild Using Deep Neural Networks: A Comprehensive Review

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Abstract

Facial expressions are a crucial aspect of human communication that provide information about emotions, intentions, interactions, and social relationships. They are a universal signal used daily to convey inner behaviors in natural situations. With the increasing interest in automatic facial emotion recognition, deep neural networks have become a popular tool for recognizing emotions in challenging in-the-wild conditions that are closer to reality. However, these systems must contend with external factors that degrade the quality of facial features, making it challenging to determine the correct emotion classes. In this paper, we first provide a summary of the various fields that use facial recognition systems under in-the-wild context. Then, we extensively examine the major datasets utilized for in-the-wild facial expression recognition, taking into account their appropriateness for this context, the challenges related to their application, the coverage of various emotions, and the potential domains of application. The analysis is conducted rigorously, emphasizing the merits and demerits of each dataset and advocating for their pertinence and effectiveness in real-life situations. We also present an expanded taxonomy of facial emotion recognition in-the-wild, while focusing mainly on deep learning methods and covering the manufacturing steps of a facial emotion recognition system and the different possible techniques for each step. Finally, we provide a discussion, insights, and conclusion, making this survey a reference point for researchers interested in the in-the-wild context, while providing a better understanding of the different datasets’ compositions and specificities. This survey can help advance research on deep facial emotion recognition in-the-wild and serve as a resource for methods, applications, and datasets in the field.

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Data Availability Statement

For all the data described in this review paper, we have provided references to the original dataset sources. Please note that the availability of these datasets may be subject to certain restrictions or usage terms imposed by the original data providers.

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Authors and Affiliations

  1. Institut Supérieur d’Informatique d’El Manar, Research Team on Intelligent Systems in Imaging and Artificial Vision (SIIVA), LR16ES06 Laboratoire de recherche en Informatique, Modélisation et Traitement de l’Information et de la Connaissance (LIMTIC), Université de Tunis El Manar, 2 Rue Abou Rayhane Bayrouni, 2080, Ariana, Tunisia

    Hadjer Boughanem, Haythem Ghazouani & Walid Barhoumi

  2. Ecole Nationale d’Ingénieurs de Carthage, Université de Carthage, 45 Rue des Entrepreneurs, 2035, Tunis-Carthage, Tunisia

    Haythem Ghazouani & Walid Barhoumi

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  1. Hadjer Boughanem
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Boughanem, H., Ghazouani, H. & Barhoumi, W. Facial Emotion Recognition in-the-Wild Using Deep Neural Networks: A Comprehensive Review. SN COMPUT. SCI. 5, 96 (2024). https://doi.org/10.1007/s42979-023-02423-7

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