Automatic Facial Expression Recognition using Convolutional Neural Network (CNN)

Authors(3) :-Eftekhar Ahmed, Tasnim Azad Abir, Jinat Ara Siraji

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.

Authors and Affiliations

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

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

  1. J. Yin "Face Festure Extraction Based on Principle Discriminant Information Analysis" ,IEEE International Conference on Automation and Logistics, 2007, pp. 1580-1584.
  2. M. Pantic and L.J.Rothkrantz "Automatic analysis of facial expressions: The state of the art. Pattern Analysis and Machine Intelligence", IEEE Transactions on, 2000, pp. 1424-1445.
  3. Ian J. Goodfellow "Challenges in representation learning: A report on three machine learning contests" , Neural information processing .2013, pp. 117-124.
  4. Z.Yu and C.Zhang "Image based static facial expression recognition with multiple deep network learning", the 2015 ACM on International Conference on Multimodal Interaction.2015, pp. 435-442.
  5. S.E.Kahou "Combining modality specific deep neural networks for emotion recognition in video" , the 15th ACM on International conference on multimodal interaction. 2013, pp. 543-550.
  6. S.Minchul "Baseline CNN structure analysis for facial expression recognition", 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) 2016, pp. 26-31.
  7. P.Ekman and W.V.Friesen "Facial Action Coding System: A Technique for the Measurement of Facial Movement", Palo Alto: Consulting Psychologists Press, 1978.
  8. T.Kanade "Recognizing action units for facial expression analysis", IEEE Transactions on Pattern Analysis and Machine Intelligence.2001, pp. 97-115.
  9. M.S. Bartlett "Fully automatic facial action recognition in spontaneous behavior", IEEE Conference on Automatic Facial and Gesture Recognition, 2006, pp. 223-230.
  10. C.Ira "Evaluation of expression recognition techniques.", Image and Video Retrieval. Springer Berlin Heidelberg, 2003, pp. 184-195.
  11. M.Liu "Deeply learning deformable facial action parts model for dynamic expression analysis" ,Computer Vision-ACCV. Springer International Publishing.2004, pp. 143-157.
  12. P.Burkert "DeXpression:Deep Convolutional Neural Network for Expression Recognition",IEEE 2015.
  13. G.Ali "Boosted NNE collections for multicultural facial expression recognition. Pattern Recognition", 2016, pp.14-27.
  14. P.Lucey "The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression", Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on IEEE.2010, pp. 94-101.
  15. M.Lyons "Coding facial expressions with gabor wavelets. Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition",1998,pp. 200-205.
  16. A.Krizhevsky "Imagenet classification with deep convolutional neural networks" Advances in neural information processing systems, 2012, pp . 1097-1105.
  17. Y.Jia "Caffe: Convolutional architecture for fast feature embedding", Eprint Arxiv.2014,pp. 675-678.
  18. K.Alex "Imagenet classification with deep convolutional neural networks", International Conference on Neural Information Processing Systems. 2012 , pp . 1097-1105

Publication Details

Published in : Volume 4 | Issue 8 | May-June 2018
Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 196-203
Manuscript Number : IJSRSET184859
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

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.
Journal URL :

Article Preview