Automatic Analysis of Facial Actions: A Survey

By Brais Martínez, Michel François Valstar, Bihan Jiang, Maja Pantic. IEEE Transactions on Affective Computing, 2019

333 citations recorded by the source corpus.

As one of the most comprehensive and objective ways to describe facial expressions, the Facial Action Coding System (FACS) has recently received significant attention. Over the past 30 years, extensive research has been conducted by psychologists and neuroscientists on various aspects of facial expression analysis using FACS. Automating FACS coding would make this research faster and more widely applicable, opening up new avenues to understanding how we communicate through facial expressions. Such an automated process can also potentially increase the reliability, precision and temporal resolution of coding. This paper provides a comprehensive survey of research into machine analysis of facial actions. We systematically review all components of such systems: pre-processing, feature extraction and machine coding of facial actions. In addition, the existing FACS-coded facial expression databases are summarised. Finally, challenges that have to be addressed to make automatic facial action analysis applicable in real-life situations are extensively discussed. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the future of machine recognition of facial actions: what are the challenges and opportunities that researchers in the field face.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/64c6c258b95a9f7c495fdf8ab5178c7cbe9bdec4

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Citations in the corpus

Deep Facial Expression Recognition: A Survey (2018, methodology): “ pre-precessing step in many facerelated recognition tasks. We list some well-known approaches and publicly available implementations that are used in deep FER. For more details, readers can refer to [25], which provides speciﬁc principles of face detection, facial landmark localization and face reg”
Facial Expression Analysis under Partial Occlusion (2018, methodology): “ A survey of FEA methods using FACS [Martinez et al. 2017].”