Facial Expression Recognition in Videos using Dynamic Kernels

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 th...

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Bibliographic Details
Published in:IEEE transactions on image processing 2020-01, Vol.PP, p.1-1
Main Authors: Perveen, Nazil, Roy, Debaditya, Mohan, C. Krishna
Format: Article
Language:English
Subjects:
Expression recognition
Face
Face recognition
factor analysis
Feature extraction
Fisher kernel
Gaussian mixture model
Hidden Markov models
intermediate matching kernel
Kernel
Kernels
MAP adaptation
Mapping
Matching
mean interval kernel
Probabilistic models
Representations
supervector kernel
universal attribute model
Videos
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Summary: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.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2020.3011846