Bayesian Analysis of Micro-Expressions: A Study on CASME II and AffectNet
Keywords:
Bayesian Framework, Emotion Recognition, Micro-Expressions, Probabilistic Models.Abstract
This paper provides a thorough investigation into utilizing a Bayesian framework to identify facial micro-expressions. The study uses two separate datasets, CASME II and AffectNet. CASME II is well-known for its high-quality videos that are specifically created to capture subtle micro-expressions in controlled settings, whereas AffectNet offers a wide range of facial expressions captured in more realistic environments. Our research utilizes sophisticated probabilistic models to improve the identification and categorization of brief facial expressions that frequently signify underlying emotions. Our objective is to tackle the difficulties presented by the nuanced and swift characteristics of micro-expressions through the utilization of Bayesian inference techniques. This study showcases the efficacy of Bayesian models in recognizing micro-expressions and emphasizes the significance of dataset characteristics in developing resilient recognition systems. The results promote additional investigation into adaptive models capable of flexibly adapting to the variability in real-world data, potentially resulting in more precise and widely applicable emotion recognition systems. The software used for conducting the experiments is Python.
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Information for the AffecNet database will be found on the link: https://paperswithcode.com/dataset/affectnet
Information for the CASMEII database will be found on the link: http://casme.psych.ac.cn/casme/e2
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