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Rank-Pooling-Based Features on Localized Regions for Automatic Micro-Expression Recognition

Rank-Pooling-Based Features on Localized Regions for Automatic Micro-Expression Recognition

Trang Thanh Quynh Le, Thuong-Khanh Tran, Manjeet Rege
Copyright: © 2020 |Volume: 11 |Issue: 4 |Pages: 13
ISSN: 1947-8534|EISSN: 1947-8542|EISBN13: 9781799805922|DOI: 10.4018/IJMDEM.2020100102
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MLA

Le, Trang Thanh Quynh, et al. "Rank-Pooling-Based Features on Localized Regions for Automatic Micro-Expression Recognition." IJMDEM vol.11, no.4 2020: pp.25-37. http://doi.org/10.4018/IJMDEM.2020100102

APA

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. http://doi.org/10.4018/IJMDEM.2020100102

Chicago

Le, Trang Thanh Quynh, Thuong-Khanh Tran, and Manjeet Rege. "Rank-Pooling-Based Features on Localized Regions for Automatic Micro-Expression Recognition," International Journal of Multimedia Data Engineering and Management (IJMDEM) 11, no.4: 25-37. http://doi.org/10.4018/IJMDEM.2020100102

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Abstract

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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