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- W3194727698 abstract "Recently, researchers focused their attention towards recognition of micro-expression due to real-time application of micro-expression recognition in human behavior understanding, as it indicates whether a person is knowingly or unknowingly manipulating their exact emotion and mental state. Recognition of micro-expression is a challenging task due to manipulated facial looks and delicate characteristic of emotion. The captioned study is an extension of our previous work on micro-expression that was implemented using discrete curvelet transform. This time, we investigate implementation of deep convolutional neural network (DCNN) for micro-expression recognition, as DCNN has established its presence in different image processing applications. CASME-II, a benchmark database for micro-expression recognition has been used for experimentations. The results of experiment had revealed that types based on CNN gives correct results of 90 and 88% for four and six class, respectively, that is beyond the regular methods. The performance comparison of our approach to previous existing methods is reported in literature." @default.
- W3194727698 created "2021-08-30" @default.
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- W3194727698 date "2021-01-01" @default.
- W3194727698 modified "2023-10-16" @default.
- W3194727698 title "An Improved DCNN Based Facial Micro-expression Recognition System" @default.
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- W3194727698 doi "https://doi.org/10.1007/978-981-16-1681-5_22" @default.
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