Facial Expression Recognition in Videos using Dynamic Kernels
| Autor: | Nazil Perveen, Krishna Mohan Chalavadi, Debaditya Roy |
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| Rok vydání: | 2020 |
| Předmět: |
Facial expression
business.industry Computer science Feature extraction Pattern recognition 02 engineering and technology Mixture model Computer Graphics and Computer-Aided Design Facial recognition system Kernel (linear algebra) Kernel (image processing) Discriminative model 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Hidden Markov model business Software |
| Zdroj: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. |
| ISSN: | 1941-0042 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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