Smiling and Non-smiling Emotion Recognition Based on Lower-half Face using Deep-Learning as Convolutional Neural Network

Malallah, Fahad and Al-Jubouri, Ahmed and Sabaawi, Abdulbasit (2020) Smiling and Non-smiling Emotion Recognition Based on Lower-half Face using Deep-Learning as Convolutional Neural Network. In: IMDC-SDSP 2020, 28-30 June 2020, Cyberspace.

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Abstract

Image understanding is considered important among researchers. In this paper, a new technique is proposed to classify a detected face into two classes as a smile or non-smile category. First, the system detects and segments only the face. Then, it converts the image from RGB to Gray-Scale and enhances the image via an equalization technique. Where the contribution of this research is depending only on the lower half of the face since most of the smiling information can be perceived from the mouth and its perimeter. Then, a convolutional neural network (CNN) is applied to generate two output nodes. Public GENKI-4K database is used for the experiments, which contains 4000 challenged face images. The results demonstrate that the accuracy with 4-Fold cross-validation is 91%. This approach achieves a promising performance as compared with the state-of-the-art techniques in both accuracy and processing time.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: computer vision classification convolutional emotion recognition machine learning neural network
Subjects: Q Science > QA Mathematics > QA76 Computer software
Depositing User: EAI Editor III.
Date Deposited: 26 Feb 2021 16:11
Last Modified: 26 Feb 2021 16:11
URI: https://eprints.eudl.eu/id/eprint/1023

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