LI, L., ZHOU, X., ZONG, Y., ZHENG, W., CHEN, X., SHI, J., & SONG, P. (2019). Unsupervised Cross-Database Micro-Expression Recognition Using Target-Adapted Least-Squares Regression. IEICE Transactions on Information and Systems, E102.D(7), 1417–1421. https://doi.org/10.1587/transinf.2018edl8174
Unsupervised cross-database micro-expression recognition using target-adapted least-squares regression
|Author:||Li, Lingyan1; Zhou, Xiaoyan1; Zong, Yuan2;|
1School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
2Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China
3Center for Machine Vision and Signal Analysis, University of Oulu, Oulu 90014, Finland
4School of Computer and Control Engineering, Yantai University, Yantai 264005, China
|Online Access:||PDF Full Text (PDF, 0.1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202001152202
Institute of Electronics, Information and Communication Engineers,
|Publish Date:|| 2020-01-15
Over the past several years, the research of micro-expression recognition (MER) has become an active topic in affective computing and computer vision because of its potential value in many application fields, e.g., lie detection. However, most previous works assumed an ideal scenario that both training and testing samples belong to the same micro-expression database, which is easily broken in practice. In this letter, we hence consider a more challenging scenario that the training and testing samples come from different micro-expression databases and investigated unsupervised cross-database MER in which the source database is labeled while the label information of target database is entirely unseen. To solve this interesting problem, we propose an effective method called target-adapted least-squares regression (TALSR). The basic idea of TALSR is to learn a regression coefficient matrix based on the source samples and their provided label information and also enable this learned regression coefficient matrix to suit the target micro-expression database. We are thus able to use the learned regression coefficient matrix to predict the micro-expression categories of the target micro-expression samples. Extensive experiments on CASME II and SMIC micro-expression databases are conducted to evaluate the proposed TALSR. The experimental results show that our TALSR has better performance than lots of recent well-performing domain adaptation methods in dealing with unsupervised cross-database MER tasks.
IEICE transactions on information and systems
|Pages:||1417 - 1421|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
This paper is supported in part by the National Key R&D Program of China under Grant 2018YFB1305200, in part by the National Basic Research Program of China under Grant 2015CB351704, in part by the National Natural Science Foundation of China under Grant 61572009, and in part by the Fundamental Research Funds for the Central Universities under Grant 2242019K40047 and Grant 2242018K3DN01.
© 2019 The Institute of Electronics, Information and Communication Engineers.