Real-time Classification of Facial Expressions Using a Principal Component Analysis and Convolutional Neural Network

Dwi Lydia Zuharah Astuti • Samsuryadi Samsuryadi • Dian Palupi Rini
Journal article Sinergi • 2019

Unduh teks lengkap
(6 pages)

Abstrak

Classification of facial expressions has become an essential part of computer systems and human-computer fast interaction. It is employed in various applications such as digital entertainment, customer service, driver monitoring, and emotional robots. Moreover, it has been studied through several aspects related to the face itself when facial expressions change based on the point of view or perspective. Facial curves such as eyebrows, nose, lips, and mouth will automatically change. Most of the proposed methods have limited frontal Face Expressions Recognition (FER), and their performance decrease when handling non-frontal and multi-view FER cases. This study combined both methods in the classification of facial expressions, namely the Principal Component Analysis (PCA) and Convolutional Neural Network (CNN) methods. The results of this study proved to be more accurate than that of previous studies. The combination of PCA and CNN methods in the Static Facial Expressions in The Wild (SFEW) 2.0 dataset obtained an accuracy amounting to 70.4%; the CNN method alone only obtained an accuracy amounting to 60.9%.

Metrik

  • 166 kali dilihat
  • 226 kali diunduh

Jurnal

Sinergi

Jurnal Ilmiah Sinergi adalah peer-reviewed jurnal yang diterbitkan 3 (tiga) kali dalam setahun, y... tampilkan semua