TY - GEN
T1 - FME 22
T2 - 30th ACM International Conference on Multimedia, MM 2022
AU - Li, Jingting
AU - Yap, Moi Hoon
AU - Cheng, Wen Huang
AU - See, John
AU - Hong, Xiaopeng
AU - Li, Xiabai
AU - Wang, Su Jing
N1 - Publisher Copyright:
© 2022 Owner/Author.
PY - 2022/10/10
Y1 - 2022/10/10
N2 - Micro-expressions are facial movements that are extremely short and not easily detected, which often reflect the genuine emotions of individuals. Micro-expressions are important cues for understanding real human emotions and can be used for non-contact non-perceptual deception detection, or abnormal emotion recognition. It has broad application prospects in national security, judicial practice, health prevention, clinical practice, etc. However, micro-expression feature extraction and learning are highly challenging because micro-expressions have the characteristics of short duration, low intensity, and local asymmetry. In addition, the intelligent micro-expression analysis combined with deep learning technology is also plagued by the problem of small samples. Not only is micro-expression elicitation very difficult, micro-expression annotation is also very time-consuming and laborious. More importantly, the micro-expression generation mechanism is not yet clear, which shackles the application of micro-expressions in real scenarios. FME'22 is the inaugural workshop in this area of research, with the aim of promoting interactions between researchers and scholars from within this niche area of research and also including those from broader, general areas of expression and psychology research. The complete FME'22 workshop proceedings are available at: https://dl.acm.org/doi/proceedings/10.1145/3552465.
AB - Micro-expressions are facial movements that are extremely short and not easily detected, which often reflect the genuine emotions of individuals. Micro-expressions are important cues for understanding real human emotions and can be used for non-contact non-perceptual deception detection, or abnormal emotion recognition. It has broad application prospects in national security, judicial practice, health prevention, clinical practice, etc. However, micro-expression feature extraction and learning are highly challenging because micro-expressions have the characteristics of short duration, low intensity, and local asymmetry. In addition, the intelligent micro-expression analysis combined with deep learning technology is also plagued by the problem of small samples. Not only is micro-expression elicitation very difficult, micro-expression annotation is also very time-consuming and laborious. More importantly, the micro-expression generation mechanism is not yet clear, which shackles the application of micro-expressions in real scenarios. FME'22 is the inaugural workshop in this area of research, with the aim of promoting interactions between researchers and scholars from within this niche area of research and also including those from broader, general areas of expression and psychology research. The complete FME'22 workshop proceedings are available at: https://dl.acm.org/doi/proceedings/10.1145/3552465.
KW - affective computing
KW - generation
KW - micro-expression
KW - spotting
UR - http://www.scopus.com/inward/record.url?scp=85148967086&partnerID=8YFLogxK
U2 - 10.1145/3503161.3554777
DO - 10.1145/3503161.3554777
M3 - Conference contribution
AN - SCOPUS:85148967086
T3 - MM 2022 - Proceedings of the 30th ACM International Conference on Multimedia
SP - 7397
EP - 7399
BT - MM 2022 - Proceedings of the 30th ACM International Conference on Multimedia
PB - Association for Computing Machinery, Inc
Y2 - 10 October 2022 through 14 October 2022
ER -