Multi-Featured and Fuzzy-Filtered Machine Learning Model for Face Expression Classification

Juneja, Kapil; Rana, Chhavi

doi:10.1007/s11277-020-07620-8

Cited by 2 publications

(1 citation statement)

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“…For accurate expression identification, an SVM classifier with various kernels is applied to this reducedfeatureset. The JAFFE and CK+ datasets are used for experimental validation by Juneja,K.,Rana,C(2020).…”

Section: Introductionmentioning

confidence: 99%

Natural Computing of Human Facial Emotion Using Multi-Learning Fuzzy Approach

Kulkarni

Rajesh

2021

International Journal of Natural Computing Research

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Emotions are described as strong feelings that are expressed by an individual in response to reactions to something or someone. Emotions are a very important aspect of day-to-day life interaction. Research shows that more than 90% of communication will happen non-verbally. This paper presents human emotion detection using a fuzzy relational model. The model consists of an image processing stage followed by an emotion recognition phase. The authors additionally made sub-categories in the most important expressions like happy and sad to discover the level of happiness and sadness in one face. Feature extraction along with multi-learning approach will help to test whether the person is truly happy or appearing to be happy. Experimental outcomes on the image dataset point out the accurate performance of the proposed technique. The experiment gives good accuracy results with the authors' own data set and robust with reference to some latest and leading edge.

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Section: Introductionmentioning

confidence: 99%