Joint Workshop KO2PI and The 1st International Conference on Advance & Scientific Innovation

Research Article

Face Expression Recognition Using Artificial Neural Network (ANN) Model Back Propagation

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  • @INPROCEEDINGS{10.4108/eai.23-4-2018.2277588,
        author={Aris Haris Rismayana},
        title={Face Expression Recognition Using Artificial Neural Network (ANN) Model Back Propagation},
        proceedings={Joint Workshop KO2PI and The 1st International Conference on Advance \& Scientific Innovation},
        publisher={EAI},
        proceedings_a={ICASI},
        year={2018},
        month={7},
        keywords={artificial neural network back propagation pca principal component analysis facial expression},
        doi={10.4108/eai.23-4-2018.2277588}
    }
    
  • Aris Haris Rismayana
    Year: 2018
    Face Expression Recognition Using Artificial Neural Network (ANN) Model Back Propagation
    ICASI
    EAI
    DOI: 10.4108/eai.23-4-2018.2277588
Aris Haris Rismayana1,*
  • 1: Politeknik TEDC1, Jl. Politeknik - Pasantren KM. 2, Cibabat, Cimahi Utara, Kota Cimahi
*Contact email: rismayana@poltektedc.ac.id

Abstract

Humans can detect a facial expressions of any person easier, because its naturally directly recognized, but its very difficult to do by machine or computer. There are 3 main stages when designing a facial expression recognition system, that is, face detection (recognizes faces), extraction of the facial expression information (feature extraction which is separate parts of the face that has information about facial expressions) and the last is the classification of the expression[1]. This research facial expression, specially for smile and not smile expression, recognition using Artificial Neural Network algorithm with Back Propagation models and optimization using Principal Component Analysis. The accuracy of the results obtained to predict the smile image is equal to 81.67%, while the accuracy for predicting not smile image is 61.67%.