Emotion Recognition on FER-2013 Face Images Using …
G.P. Kusuma et al. / Advances in Science, Technology and Engineering Systems Journal Vol. 5, No. 6, 315-322 (2020) 317 and 45.95% accuracy on test data of FER-2013 dataset
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No Active Events. Create notebooks and keep track of their status here.
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Introduction. FER2013 is a challenging datasets with human-level accuracy only at 65 ± 5 %. It is not a balanced dataset, as it contains images of 7 facial expressions, with …
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Usage: - Use array_to_image.ipynb to transform the array in fer_2013.csv to image. - Modify camera.py to suit your purpose. - In Line 6/7, you can choose to use version 1 or version 2.
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Previously, we have leveraged the idea of dense connectivity to design an effective FER-based dense connected network and has achieved good results [17]. Ryan et al. [19] …
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The model was chosen taking inspiration from the work of (Vulpe-Grigoraşi and Grigore 2021), in this paper they used Random Search Algorithms which are an automated method capable of generating models with various architectures and different configurations of hyperparameters from a discrete space of possible solutions that can be applied in the …
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So based on this research, on the FER2013 dataset, CNN using the Adamax optimizer produced a fairly good performance where the value is given is 66% compared to Adam, N-Adam, and SGD.Keywords - CNN, Facial Expression, FER-2013
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WhatsApp: +86 18221755073The Facial Emotion Recognition (FER-2013) Dataset for …
One of the ways humans communicate is by using facial expressions. Research on technology development in artificial intelligence uses deep learning methods in human and computer interactions as an effective system application process. One example, if someone does show and tries to recognize facial expressions when communicating. The …
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