Multiview Facial Expression Recognition, a Survey

Mahdi Jampour and Malihe Javidi

Special congrats and thanks to my colleague, Dr. Malihe Javidi, for her impressive collaboration in our recent research work. Our paper titled “Multiview Facial Expression Recognition, a Survey” is now accepted for publication in prestigious and outstanding IEEE Transactions on Affective Computing. TAFFC is within the Top 10% of journals in AI with Impact Factor 13.99.



Multiview Facial Expression Recognition (MFER) is a well-known interdisciplinary problem among computer science and related disciplines with promising and valuable applications. Recognizing the facial expression in pose variations, which is very common in real-world conditions, makes it very challenging. This paper aims to provide a comprehensive survey of the MFER progress, including both categories of traditional and deep approaches. In general, we sort each of these categories into three overall groups to meet the pose variations: Pose-Robust Features, Pose Normalization, and Pose-Specific Classification. While reviewing the traditional methods, a thorough study is proposed on the existing novel deep techniques. We also introduce the state-of-the-art and discuss the challenges, limitations, opportunities, and future trends that need to be addressed in this field. Moreover, we provide an extensive review of publicly available datasets for MFER, including the labs' collections and the sets gathered from in the wild. Besides, we introduce the most popular protocols on each dataset to standardize comparisons in the future.


Link to the paper:


Cite our paper if you find it useful:

Mahdi Jampour and Malihe Javidi,
"Multiview Facial Expression Recognition, a Survey,"
in IEEE Transactions on Affective Computing, 2022, doi: 10.1109/TAFFC.2022.3184995

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