The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Towards Reading Hidden Emotions: A Comparative Study of Spontaneous Micro-Expression Spotting and Recognition Methods
Open accessAbstract
Micro-expressions (MEs) are rapid, involuntary facial expressions which reveal emotions that people do not intend to show. Studying MEs is valuable as recognizing them has many important applications, particularly in forensic science and psychotherapy. However, analyzing spontaneous MEs is very challenging due to their short duration and low intensity. Automatic ME analysis includes two tasks: ME spotting and ME recognition. For ME spotting, previous studies have focused on posed rather than spontaneous videos. For ME recognition, the performance of previous studies is low. To address these challenges, we make the following contributions: (i) We propose the first method for spotting spontaneous MEs in long videos (by exploiting feature difference contrast). This method is training free and works on arbitrary unseen videos. (ii) We present an advanced ME recognition framework, which outperforms previous work by a large margin on two challenging spontaneous ME databases (SMIC and CASMEII). (iii) We propose the first automatic ME analysis system (MESR), which can spot and recognize MEs from spontaneous video data. Finally, we show our method outperforms humans in the ME recognition task by a large margin, and achieves comparable performance to humans at the very challenging task of spotting and then recognizing spontaneous MEs.
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Multimodal data to design visual learning analytics for understanding regulation of learning
Noroozi, O., Alikhani, I., Järvelä, S., Kirschner, P. A., Juuso, I., & Seppänen, T. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.12.019 · Cited by 140
15 challenges for AI: Or what AI (currently) can’t do
Hagendorff, T., & Wezel, K. (2020) · AI & SOCIETY · doi:10.1007/s00146-019-00886-y · Cited by 113
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Metadata
- Authors
- Li, X., Hong, X., Moilanen, A., Huang, X., Pfister, T., Zhao, G., & Pietikainen, M.
- Venue
- IEEE Transactions on Affective Computing
- Published
- 2018-10-01
- Volume
- 9
- Issue
- 4
- Pages
- 563-577
- ISSN
- 1949-3045 (ISSN-L)
- 2371-9850
- OpenAlex
- W2587585332
- Cited by
- 323
- References
- 49
- FWCI
- 28.19
- Type
- journal article
- Language
- en
- Open access
- green
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- The paper
- Li, X., Hong, X., Moilanen, A., Huang, X., Pfister, T., Zhao, G., & Pietikainen, M. (2018). Towards Reading Hidden Emotions: A Comparative Study of Spontaneous Micro-Expression Spotting and Recognition Methods. IEEE Transactions on Affective Computing. https://doi.org/10.1109/taffc.2017.2667642
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