Facial Expression Recognition in Videos Using Dynamic Kernels

Perveen, Nazil and Roy, Debaditya and Mohan, C Krishna (2020) Facial Expression Recognition in Videos Using Dynamic Kernels. IEEE Transactions on Image Processing, 29. pp. 8316-8325. ISSN 1057-7149

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Abstract

Recognition of facial expressions across various actors, contexts, and recording conditions in real-world videos involves identifying local facial movements. Hence, it is important to discover the formation of expressions from local representations captured from different parts of the face. So in this paper, we propose a dynamic kernel-based representation for facial expressions that assimilates facial movements captured using local spatio-temporal representations in a large universal Gaussian mixture model (uGMM). These dynamic kernels are used to preserve local similarities while handling global context changes for the same expression by utilizing the statistics of uGMM. We demonstrate the efficacy of dynamic kernel representation using three different dynamic kernels, namely, explicit mapping based, probability-based, and matching-based, on three standard facial expression datasets, namely, MMI, AFEW, and BP4D. Our evaluations show that probability-based kernels are the most discriminative among the dynamic kernels. However, in terms of computational complexity, intermediate matching kernels are more efficient as compared to the other two representations. © 1992-2012 IEEE.

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IITH Creators:

IITH Creators	ORCiD
Mohan, C Krishna	https://orcid.org/0000-0002-7316-0836

Item Type:

Article

Uncontrolled Keywords:

Expression recognition; factor analysis; feature extraction; fisher kernel; Gaussian mixture model; intermediate matching kernel; MAP adaptation; mean interval kernel; supervector kernel; universal attribute model

Subjects:

Computer science

Divisions:

Department of Computer Science & Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

04 Nov 2022 09:37

Last Modified:

04 Nov 2022 09:37

URI:

http://raiithold.iith.ac.in/id/eprint/11154