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

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.

Le Trang Thanh Quynh, Tran Thuong-Khanh, Rege Manjeet

Publication type:
A1 Journal article – refereed

Place of publication:

Dynamic Image, Image classification, micro-expression


Full citation:
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


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