Yee-Hui Oh, John See, Anh Cat Le Ngo, Raphael C. -W. Phan, Vishnu M. Baskaran
Abstract
Over the last few years, automatic facial micro-expression analysis has garnered increasing attention from experts across different disciplines because of its potential applications in various fields such as clinical diagnosis, forensic investigation and security systems. Advances in computer algorithms and video acquisition technology have rendered machine analysis of facial micro-expressions possible today, in contrast to decades ago when it was primarily the domain of psychiatrists where analysis was largely manual. Indeed, although the study of facial micro-expressions is a well-established field in psychology, it is still relatively new from the computational perspective with many interesting problems. In this survey, we present a comprehensive review of state-of-the-art databases and methods for micro-expressions spotting and recognition. Individual stages involved in the automation of these tasks are also described and reviewed at length. In addition, we also deliberate on the challenges and future directions in this growing field of automatic facial micro-expression analysis.
This research output has an Altmetric Attention Score of 13. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 April 2024.
All research outputs
#2,886,328
of 26,411,386 outputs
Outputs from Frontiers in Psychology
#5,718
of 35,542 outputs
Outputs of similar age
#53,907
of 342,795 outputs
Outputs of similar age from Frontiers in Psychology
#163
of 722 outputs
Altmetric has tracked 26,411,386 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 35,542 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.9. This one has done well, scoring higher than 83% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 342,795 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 722 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.
