University of Oulu

Le, T. T., Tran, T., & Rege, M. (2020). Rank-Pooling-Based Features on Localized Regions for Automatic Micro-Expression Recognition. International Journal of Multimedia Data Engineering and Management (IJMDEM), 11(4), 25-37. doi:10.4018/IJMDEM.2020100102

Rank-pooling-based features on localized regions for automatic micro-expression recognition

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Author: Le, Trang Thanh Quynh1; Tran, Thuong-Khanh2; Rege, Manjeet1
Organizations: 1University of St. Thomas, USA
2University of Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
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Language: English
Published: IGI Global, 2020
Publish Date: 2021-03-03


Facial micro-expression is a subtle and involuntary facial expression that exhibits short duration and low intensity where hidden feelings can be disclosed. The field of micro-expression analysis has been receiving substantial awareness due to its potential values in a wide variety of practical applications. A number of studies have proposed sophisticated hand-crafted feature representations in order to leverage the task of automatic micro-expression recognition. This paper employs a dynamic image computation method for feature extraction so that features can be learned on certain localized facial regions along with deep convolutional networks to identify micro-expressions presented in the extracted dynamic images. The proposed framework is simple as opposed to other existing frameworks which used complex hand-crafted feature descriptors. For performance evaluation, the framework is tested on three publicly available databases, as well as on the integrated database in which individual databases are merged into a data pool. Impressive results from the series of experimental work show that the technique is promising in recognizing micro-expressions.

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Series: International journal of multimedia data engineering & management
ISSN: 1947-8534
ISSN-E: 1947-8542
ISSN-L: 1947-8534
Volume: 11
Issue: 4
Pages: 25 - 37
DOI: 10.4018/IJMDEM.2020100102
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
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