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Development of video-based emotion recognition using deep learning with Google Colab
[...]frames are to be extracted from the input video [4]. Face detection Emotions are featured mainly from the face. [...]it is crucial to detect the face to obtain facial features for further processing and recognition. [...]resizing is very important to shorten the processing time. [...]better res...
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Published in: | Telkomnika 2020-10, Vol.18 (5), p.2463-2471 |
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container_title | Telkomnika |
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creator | Gunawan, Teddy Surya Ashraf, Arselan Riza, Bob Subhan Haryanto, Edy Victor Rosnelly, Rika Kartiwi, Mira Janin, Zuriati |
description | [...]frames are to be extracted from the input video [4]. Face detection Emotions are featured mainly from the face. [...]it is crucial to detect the face to obtain facial features for further processing and recognition. [...]resizing is very important to shorten the processing time. [...]better resizing techniques should be used to preserve image attributes after resizing [8]. The accuracy of the classification depends on whether the features are well representing the expression or not. [...]the optimization of the selected features will automatically improve classification accuracy [9]. |
doi_str_mv | 10.12928/telkomnika.v18i5.16717 |
format | article |
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subjects | Accuracy Algorithms Architecture Artificial intelligence Deep learning Emotions Face recognition Human-computer interaction Image classification Machine learning Neural networks Optimization Researchers |
title | Development of video-based emotion recognition using deep learning with Google Colab |
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