Face Expression Recognition and Analysis: The State of the Art

By Vinay Bettadapura. arXiv.org, 2012

366 citations recorded by the source corpus.

The automatic recognition of facial expressions has been an active research topic since the early nineties. There have been several advances in the past few years in terms of face detection and tracking, feature extraction mechanisms and the techniques used for expression classification. This paper surveys some of the published work since 2001 till date. The paper presents a time-line view of the advances made in this field, the applications of automatic face expression recognizers, the characteristics of an ideal system, the databases that have been used and the advances made in terms of their standardization and a detailed summary of the state of the art. The paper also discusses facial parameterization using FACS Action Units (AUs) and MPEG-4 Facial Animation Parameters (FAPs) and the recent advances in face detection, tracking and feature extraction methods. Notes have also been presented on emotions, expressions and facial features, discussion on the six prototypic expressions and the recent studies on expression classifiers. The paper ends with a note on the challenges and the future work. This paper has been written in a tutorial style with the intention of helping students and researchers who are new to this field.

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

View Paper: https://www.semanticscholar.org/paper/b33e8db8ccabdfc49211e46d78d09b14557d4cba

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In Experimental and Cognitive Psychology

Citations in the corpus

50 years of biometric research: Accomplishments, challenges, and opportunities (2016, neutral): “JID: PATREC [m5G; April 22, 2016;10:30 ] Fig .”
Reliable Crowdsourcing and Deep Locality-Preserving Learning for Unconstrained Facial Expression Recognition (2019, neutral): “Furthermore, anger and disgust are often confused with each other in both cases, which conforms to the survey in [5].”
Face Alignment in Full Pose Range: A 3D Total Solution (2015, background): “It is an essential preprocessing step for many face analysis tasks, e.g., face recognition [41], expression recognition [5] and inverse rendering [1].”
Facial Expression Analysis under Partial Occlusion (2018, background): “…analyzing deliberately posed prototypical emotions of near-frontal faces collected in controlled laboratory settings to spontaneously evoked emotions collected from non-constrained settings (see reviews [Pantic and Rothkrantz 2000],[Fasel and Luettin 2003],[Zeng, et al. 2009],[Bettadapura 2012]).”
Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild (2017, result): “Besides, anger and disgust are usually confused with each other in both cases, which conforms to the survey in [2].”
Towards Fast, Accurate and Stable 3D Dense Face Alignment (2020, background): “, recognition [42,6,25,23,12], animation [9], avatar retargeting [8], tracking [47], attribute classification [3,21,20], image restoration [48,11,10], anti-spoofing [46,51,38,50,24].”