X. Li et al., "Towards Reading Hidden Emotions: A Comparative Study of Spontaneous Micro-Expression Spotting and Recognition Methods," in IEEE Transactions on Affective Computing, vol. 9, no. 4, pp. 563-577, 1 Oct.-Dec. 2018. doi: 10.1109/TAFFC.2017.2667642
Towards reading hidden emotions : a comparative study of spontaneous micro-expression spotting and recognition methods
|Author:||Li, Xiaobai1; Hong, Xiaopeng1; Moilanen, Antti1;|
1Center for Machine Vision and Signal Analysis, University of Oulu, Oulu 90014, Finland
2Department of Engineering Science, University of Oxford, Oxford OX1 3PA, United Kingdom
|Online Access:||PDF Full Text (PDF, 5.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019060618851
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2019-06-06
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.
IEEE transactions on affective computing
|Pages:||563 - 577|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
113 Computer and information sciences
This work was sponsored by the Academy of Finland, Infotech Oulu and Tekes Fidipro program. Guoying Zhao is the corresponding author of this paper.
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