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Automatic Facial Expression Recognition using Convolutional Neural Network (CNN)


Eftekhar Ahmed, Tasnim Azad Abir, Jinat Ara Siraji
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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, CNNís 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.

Eftekhar Ahmed, Tasnim Azad Abir, Jinat Ara Siraji

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

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Publication Details

Published in : Volume 4 | Issue 8 | May-June - 2018
Date of Publication Print ISSN Online ISSN
2018-06-30 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
196-203 IJSRSET184859   Technoscience Academy

Cite This Article

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.
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