Proceedings of the 1st International Multi-Disciplinary Conference Theme: Sustainable Development and Smart Planning, IMDC-SDSP 2020, Cyperspace, 28-30 June 2020

Research Article

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

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  • @INPROCEEDINGS{10.4108/eai.28-6-2020.2298175,
        author={Fahad  Malallah and Ahmed  Al-Jubouri and Abdulbasit  Sabaawi},
        title={Smiling and Non-smiling Emotion Recognition Based on Lower-half Face using Deep-Learning as Convolutional Neural Network},
        proceedings={Proceedings of the 1st International Multi-Disciplinary Conference Theme: Sustainable Development and Smart Planning, IMDC-SDSP 2020, Cyperspace, 28-30 June 2020},
        publisher={EAI},
        proceedings_a={IMDC-SDSP},
        year={2020},
        month={9},
        keywords={computer vision classification convolutional emotion recognition machine learning neural network},
        doi={10.4108/eai.28-6-2020.2298175}
    }
    
  • Fahad Malallah
    Ahmed Al-Jubouri
    Abdulbasit Sabaawi
    Year: 2020
    Smiling and Non-smiling Emotion Recognition Based on Lower-half Face using Deep-Learning as Convolutional Neural Network
    IMDC-SDSP
    EAI
    DOI: 10.4108/eai.28-6-2020.2298175
Fahad Malallah1,*, Ahmed Al-Jubouri2, Abdulbasit Sabaawi1
  • 1: Computer and Information, College of Electronics Engineering, Ninevah University, Mosul, Iraq
  • 2: 1Computer and Information, College of Electronics Engineering, Ninevah University, Mosul, Iraq
*Contact email: Fahad.malallah@uoninevah.edu.iq

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.