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Azmi Najid
Universitas Indonesia
Computer Science & IT Electrical & Electronics Engineering

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Visual Emotion Recognition Using ResNet Azmi Najid; Dina Chahyati
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (944.458 KB) | DOI: 10.11591/eecsi.v5.1700

Abstract

Given an image, humans have emotional reactions to it such as happy, fear, disgust, etc. The purpose of this research is to classify images based on human's reaction to them using ResNet deep architecture. The problem is that emotional reaction from humans are subjective, therefore a confidently labelled dataset is difficult to obtain. This research tries to overcome this problem by implementing and analyzing transfer learning from a big dataset such as ImageNet to relatively small visual emotion dataset. Other than that, because emotion is determined by low-level and high-level features, we will make a modification to a pretrained residual network to better utilize low-level and high-level feature to be used in visual emotion recognition. Results show that general (low-level) features and specific (high-level) features obtained from ImageNet object recognition can be well utilized for visual emotion recognition.
Co-Authors Dina Chahyati