SAMM: A Spontaneous Micro-Facial Movement Dataset
By Adrian K. Davison, Cliff Lansley, N. Costen, Kevin Tan, Moi Hoon Yap. IEEE Transactions on Affective Computing, 2018
635 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Neurology
View Paper: https://www.semanticscholar.org/paper/3e8355b608099a0d5564b6a6d8ecd8b505007251
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Yong-jin Liu
- Xiaopeng Hong
- Wei Peng
- Yingyue Xu
- Su-Jing Wang
- Guoying Zhao
- Sze‐Teng Liong
- Junping Zhang
In Experimental and Cognitive Psychology
- Yong-jin Liu
- Xiaopeng Hong
- Wei Peng
- Yingyue Xu
- Su-Jing Wang
- Sze‐Teng Liong
- Guoying Zhao
- Kidiyo Kpalma
In Neurology
Citations in the corpus
- A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, background): “SAMM [81] 2018 159 sequences 32 Six basic expressions plus Others”
- Video-Based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms (2021, methodology): “The SAMM dataset [29] contains 159 samples (i.”
- A Boost in Revealing Subtle Facial Expressions: A Consolidated Eulerian Framework (2019, background): “Promising progresses have been made over the past decade, in aspects such as dataset construction [15], [34], [4], spatial-temporal descriptors either by hand-crafting [3], [36], [14], [39], [9], [33], [7], [40], or deep learning [23], [26], [16], [31], [32], [8], [29], etc.”
- OFF-ApexNet on Micro-expression Recognition System (2018, methodology): “For methods #1 to #11, the number of the expression to be predicted are based on the suggested expression category from the original papers [15–17].”