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Geometry classification model which was pretrained on ImageNet dataset and fine-tuned for emotion classification. The classification is performed on the publicly available FER-2013 dataset of over 35,000 face images with in-the-wild setting for 7 distinct emotions with the provided 80% training, 10% validation, and 10% testing data distributions. The proposed approach outperforms most standalone-based model results with 69.40% accuracy.
Kusuma et al. (Wed,) studied this question.
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