Open AccessFacial Emotion Detection Using Haar-Cascade Classifier and Convolutional Neural Networks
Author(s) -
Prismahardi Aji Riyantoko,
_ Sugiarto,
Kartika Maulida Hindrayani
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1844/1/012004
Subject(s) - convolutional neural network , haar like features , artificial intelligence , computer science , facial expression , pattern recognition (psychology) , classifier (uml) , haar , emotion recognition , cascade , facial expression recognition , speech recognition , computer vision , facial recognition system , face detection , chemistry , chromatography , wavelet
Computer vision has the challenge to detect the facial emotions of humans. Recently, in computer vision and machine learning, it’s possible to detect emotion from video or image accurate. In our research will propose to classify facial emotion using Haar-Cascade Classifier and Convolutional Neural Networks. The experiment uses the FER2013 dataset which was collected for the facial expression recognition dataset, and we proposed seven classified facial expression. The CNN model gain MSE and accuracy value based on epoch variety. The results showed that with the increase in the epoch value, the smaller MSE value would be obtained, likewise, the accuracy value would be increased. Thus, the proposed algorithm of CNN is proven to be effective for facial emotion detection.