Authors :
Dwi Lydia ZA, Samsuryadi, Dian Palupi Rini.
Volume/Issue :
Volume 4 - 2019, Issue 6 - June
Google Scholar :
https://goo.gl/DF9R4u
Scribd :
https://bit.ly/2ZYiTqs
Abstract :
Classification of facial expressions is rapidly becoming an important part of computer systems, and interactions between humans and computers. Because the most expressive way of showing human emotions is through facial expressions. Classification of facial expressions is studied through several aspects related to the face itself. When facial expressions change, then the curves on the face such as eyebrows, nose, lips and mouth will automatically change. This study combines real-time facial expressions classification using the Principal Component Analysis (PCA) and Convolutional Neural Network (CNN) methods. This study showed higher results in 9,5% of previous studies using the CNN method.
Keywords :
Classification, Facial Recognition, Image Recognition, Convolutional Neural Network, Principal Component Analysis.
Classification of facial expressions is rapidly becoming an important part of computer systems, and interactions between humans and computers. Because the most expressive way of showing human emotions is through facial expressions. Classification of facial expressions is studied through several aspects related to the face itself. When facial expressions change, then the curves on the face such as eyebrows, nose, lips and mouth will automatically change. This study combines real-time facial expressions classification using the Principal Component Analysis (PCA) and Convolutional Neural Network (CNN) methods. This study showed higher results in 9,5% of previous studies using the CNN method.
Keywords :
Classification, Facial Recognition, Image Recognition, Convolutional Neural Network, Principal Component Analysis.
