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- W3107418250 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." @default.
- W3107418250 created "2020-12-07" @default.
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- W3107418250 date "2020-10-01" @default.
- W3107418250 modified "2023-09-23" @default.
- W3107418250 title "Rank-Pooling-Based Features on Localized Regions for Automatic Micro-Expression Recognition" @default.
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- W3107418250 doi "https://doi.org/10.4018/ijmdem.2020100102" @default.
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