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- W4312901035 abstract "The age estimation from facial expression is most rapid topic of interest, these days because of its great applications. It is a challenging task because of huge intra-class facial expression differences due to age. In this study, a methodology has been proposed to estimate the age depending upon the expressions of face of a human. The proposed methodology initiates its process with clarification of facial expression which is followed by the clustering process. The images of the database are clustered based on expressions. For this purpose, six facial expressions (Happiness, Disgust, Anger, Fear, surprise and sadness) are considered in this study. For getting better accuracy in age estimation, the features are extracted cluster wise using Binarized Statistical image feature (BSIF) in multi-layer fashion. The final feature vector is feed to ensemble auto-encoder (EAE) to train and estimate age by considering min Error value. The age categories considered are four: child (0-10), young (11-30), Middle (31-50) and senior citizen (50+). To estimate age, this study considered two databases namely: MORPH-II, PAL. The test results on the chosen databases perform better in terms of estimation of age when contrasted with other methods of the state of art." @default.
- W4312901035 created "2023-01-05" @default.
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- W4312901035 date "2022-07-15" @default.
- W4312901035 modified "2023-09-27" @default.
- W4312901035 title "An Ensemble Auto-Encoder for Age Estimation Based on Human Facial Expressions" @default.
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- W4312901035 doi "https://doi.org/10.1109/icses55317.2022.9914215" @default.
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