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Challenges and Emerging Trends for Machine Reading of the Mind from Facial Expressions

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Abstract

Facial emotion recognition (FER) has garnered substantial attention in computer vision and machine learning domains due to its versatile applications. Nonetheless, achieving accurate recognition of facial expressions presents significant challenges. In this paper, we embark on a thorough investigation of FER methodologies, encompassing classical and handcrafted approaches as well as advanced deep learning architectures. Then, we comprehensively analyze the encountered challenges in FER, encompassing contextual and psychological challenges, technical challenges, and ethical considerations. By carefully identifying the research gaps arising from these challenges, we present a rigorous review of emerging trends in FER, focusing on recent studies that address these challenges. In delving into these partially explored fields, our aim is to present a comprehensive overview of pertinent research endeavors aimed at addressing challenges in FER. Additionally, we seek to shed light on areas that still remain unexplored and require further investigation. The analyzed emerging trends encompass context-aware models, exploration of spatial-temporal information, and the application of emerging data augmentation techniques, among others. Furthermore, we discuss advancements in feature extraction techniques and provide insightful recommendations for future research directions. This comprehensive examination aims to enhance the understanding of FER and facilitate the development of more robust and accurate facial emotion recognition systems.

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Data availability

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

    Haythem Ghazouani

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

    Haythem Ghazouani

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Ghazouani, H. Challenges and Emerging Trends for Machine Reading of the Mind from Facial Expressions. SN COMPUT. SCI. 5, 103 (2024). https://doi.org/10.1007/s42979-023-02447-z

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