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• W4384103885 endingPage "106661" @default.
• W4384103885 startingPage "106661" @default.
• W4384103885 abstract "In the process of day-to-day learning, expressions are very vital. Detection of human expressions is growing and has caught the interest of many researchers in the past few years, to facilitate research on Affective Computing. Classification of expressions is necessarily needed to keep focus on several expressions for developing an efficient electronic learning system. This classification can provide several solutions in the application of psychology, human computer interaction, artificial intelligence, education, gaming etc. The core attention of this review is to highlight the most recent algorithms used for the extraction of features from multi-modal datasets and various classifiers that are used in the discipline of Multi-modal expression detection (MED). In this study, we review the literature on MED between 2015 and 2022, focusing mainly on feature extraction and classifier schemes. Their advantages, disadvantages and the entire vision have been discussed in brief. This survey also shows that the efficiency of the MED environment is better as compared to single-modal ones." @default.
• W4384103885 created "2023-07-13" @default.
• W4384103885 creator A5006712665 @default.
• W4384103885 creator A5039425199 @default.
• W4384103885 date "2023-10-01" @default.
• W4384103885 modified "2023-10-17" @default.
• W4384103885 title "Multi-modal Expression Detection (MED): A cutting-edge review of current trends, challenges and solutions" @default.
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