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- W3209177060 abstract "In this paper, a frontal facial expressions recognition classifier is developed based on a deep convolutional model with a few numbers of learning parameters that make the proposed model applicable for mobile applications. In this, a high rate of accuracy and fast recognition is achieved. Firstly, contrast enhancement of the input images of the FER+ dataset is applied using the adaptive histogram equalization method. Then, the frontal facial images are only selected by using Haar-Cascade descriptors. Moreover, three different training methods are examined and the best recognition outcome of the presented CNN model is chosen, which outperforms the compared CNN models in terms of correctness and reduced number of parameters. The average accuracy of the proposed model reaches 94 % on validation and test sets. The acquired result can be considered promising in the domain of real-time communication with machines. Stating that feature selection and image enhancement have an effective impact on the precision of the emotion classifier. However, the shortage of datasets in any emotion class can defect the learning process and cause an instant decrease in the classifier accuracy." @default.
- W3209177060 created "2021-11-08" @default.
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- W3209177060 date "2021-08-27" @default.
- W3209177060 modified "2023-09-23" @default.
- W3209177060 title "Frontal Facial Expression Recognition Based on Convolutional Neural Network and Haar-Cascade Descriptors" @default.
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- W3209177060 doi "https://doi.org/10.1109/rteict52294.2021.9573915" @default.
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