Covariance pooling for facial expression recognition

Classifying facial expressions into different categories requires capturing regional distortions of facial landmarks. We believe that second-order statistics such as covariance is better able to capture such distortions in regional facial features. In this work, we explore the benefits of using a ma...

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Bibliographic Details
Main Authors: ACHARYA, D., HUANG, Zhiwu, PAUDEL, D., VAN, Gool L.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2018
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Online Access:https://ink.library.smu.edu.sg/sis_research/6388
https://ink.library.smu.edu.sg/context/sis_research/article/7391/viewcontent/Covariance_Pooling_for_Facial_Expression_Recognition.pdf
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Institution: Singapore Management University
Language: English

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