Automatic Facial Expression Recognition using Convolutional Neural Network (CNN)

Authors

  • Eftekhar Ahmed  Department of Electronics and Communication Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh
  • Tasnim Azad Abir  Department of Electronics and Communication Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh
  • Jinat Ara Siraji  Department of Electronics and Communication Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh

Keywords:

Convolutional Neural Network, Distributed Neural Network, Support Vector Machine, Principle Component Analysis

Abstract

Facial Expression Recognition is has been widely used in Artificial Intelligence, Human-Computer Interaction, and Security Monitoring. Convolution neural network (CNN) works as a depth learning architecture and it can extract the essential features of the image. In the case of large changes in shooting conditions, CNN’s effect is better than the methods of Support Vector Machines (SVM) and Principal Component Analysis (PCA). Therefore, we are proposing a method based on CNN. The purpose is to classify each facial image as one of the seven facial expressions considered here. A new convolution neural network structure has been designed according to the characteristics of facial expression recognition. To extract implicit features convolution kernel is being used and max-pooling is being used to reduce the dimensions of the extracted implicit features. In comparison to AlexNet network, we can improve the recognition accuracy about on the FER and CK+ facial expression database with the help of Batch Normalization (BN) layer to our network. A facial expression recognition system is constructed for the convenience of application, and all the experimental results show that the system can reach the real-time needs.

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Published

2018-06-30

Issue

Section

Research Articles

How to Cite

[1]
Eftekhar Ahmed, Tasnim Azad Abir, Jinat Ara Siraji, " Automatic Facial Expression Recognition using Convolutional Neural Network (CNN) , International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 8, pp.196-203, May-June-2018.