Jan 23, 2019 · Almost all CNN models known to achieve high accuracy on facial expression recognition problem are influenced by these architectures. This work ...
In this work, the effect of CNN parameters namely kernel size and number of filters on the classification accuracy is investigated using FER-2013 dataset.
This work proposes two novel CNN architectures which achieve a human-like accuracy of 65% and can serve as a basis for standardization of the base model for ...
Jan 23, 2019 · In this work, the effect of CNN parameters namely kernel size and number of filters on the classification accuracy is investigated using FER-2013 dataset.
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We investigate whether the well-known poor performance of the head-on usage of the convolutional neural networks for the facial expression recognition task may ...
[5] Agrawal A, Mittal N. Using CNN for facial expression recognition: a study of the effects of kernel size and number of filters on accuracy[J]. The Visual ...
The purpose of this paper is to make a study on recent works on automatic facial emotion recognition FER via deep learning.
A Deep CNN Architecture for Facial Expression Recognition in the Wild
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Using CNN for facial expression recognition: a study of the effects of kernel size and number of filters on accuracy · Abhinav AgrawalNamita Mittal. Computer ...
A new technique called Fusion-CNN is proposed. It improves accuracy by extracting hybrid features using a β-skeleton undirected graph and an ellipse.
Sep 16, 2024 · "Using CNN for facial expression recognition: a study of the effects of kernel size and number of filters on accuracy." The Visual Computer ...