Micro-expression spotting based on supervised learning

Thesis event information

Date and time of the thesis defence

Place of the thesis defence

L10, Linnanmaa campus

Topic of the dissertation

Micro-expression spotting based on supervised learning

Doctoral candidate

Master of Engineering Thuong-Khanh Tran

Faculty and unit

University of Oulu Graduate School, Faculty of Information Technology and Electrical Engineering, Center for Machine Vision and Signal Analysis

Subject of study

Computer Science and Engineering


Professor Liming Chen, École centrale de Lyon


Academy Professor Guoying Zhao, University of Oulu

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Micro-expression spotting based on supervised learning

Micro-expressions are fast and involuntary facial expressions, which indicate concealed or suppressed emotions. Recently, micro-expression analysis has become an attractive topic. There have been many studies involved in this problem due to potential applications for assisting human-centered interactions and communication. However, there are still limitations, especially in micro-expression spotting. This is one task of micro-expression analysis for locating the temporal position of micro-expressions in video sequence.

This thesis is a thorough summary of the main topic which deals with exploring supervised learning for micro-expression spotting consisting of five publications. Firstly, the data preparation and benchmarking for micro-expressions spotting are presented, including video frame selection, face image alignment, and new benchmarks for micro-expressions spotting to standardize the evaluation of micro-expressions spotting. Secondly, machine learning models for facial expression analysis and micro-expressions spotting are introduced, from handcrafted features to deep learning techniques.

The contributions of this research are as follows. Firstly, I extend a spontaneous micro-expressions database to create a larger version to achieve better micro-expression spotting tasks. Additionally, I introduce new benchmarks for micro-expressions spotting to obtain a fair comparison for micro-expressions spotting techniques. Moreover, I introduce our findings to a new method based on the multi-modal approach to spot micro-expression in long videos. Finally, I summarize the contributions of the work and discuss future research.
Last updated: 23.1.2024