Neural Network-Based Expression Recognition System for Static Facial Images
Keywords:
facial feature, neural network, sobel edge, expression classificationAbstract
Affective Computing is a field of studying the human effect to interpret, recognize, process, and simulate in computer science, psychology, and cognitive science. Humans express their emotions in a variety of ways such as body gesture, word, vocal, and mainly facial expression. Non-verbal behavior is a significant component of communication, and facial expressions of emotions are the most important complex signal. Facial Expression Recognition (FER) is an interesting and challenging task in artificial intelligence. FER system in the study three steps including preprocessing, feature extraction and expression classification. In the paper, comparative analysis of expression recognition is implemented based on Neural Network (NN) with three feature extraction methods of Sobel Edge, Histogram of Oriented Gradient and Local Binary Pattern. NN-based expression recognition system achieves an accuracy of 95.82% and 97.68% for JAFFE and CK+ dataset respectively. The result has shown that the Edge features are the effected features for recognizing human expression using still images.
References
. D. M, Aung and N. Aye, “A Facial Expression Classification using Histogram Based Method,” 2012 4th International Conference on Signal Procession Systems (ICSPS), vol. 58, pp. 1, 2012.
. D. Yang, P.W.C. Abeer Alsadoon, A.K. Dingh, A.Elchouemi, “An Emotion Recognition Model Based on Facial Recognition in Virtual Learning Environment,”. Procedia Computer Science 125 (2018) 2-10.
. T. Kalsum, S.M.Anwar, M. Majid, B. Khan, and S.M. Ali, “Emotion recognition from facial expressions using hybrid feature descriptors,” 12 (6), IET Image Processing, 1004-1012.
. D. V. Sang, and N. Van Dat, (2017, October) “Facial expression recognition using deep convolutional neural networks,” In Knowledge and Systems Engineering (KSE), 2017 9th International Conference on, pp. 130-135, IEEE.
. B.F. Wu, and C.H. Lin “Adaptive Feature Mapping for Customizing Deep Learning Based Facial Expression Recognition mode,” IEEE Access 6, 2018; 12451-12461.
. Qi, Chao, et al. “Facial Expressions Recognition Based on Cognition and Mapped Binary Patterns,” (2018): 18759-18803, IEEE Access 6.
. A. M. Shabat and J.R. Tapamo, “Angled local directional pattern for texture analysis with an application to facial expression recognition,” ISSN 1751-9632, IET Computer Vision (February 2018)
. N. P. N. Sreedharan, B. Ganesan, R. Raveendran, P. Sarala, and B. Dennis, (September, 2018) “Grey Wold optimization-based feature selection and classification for facial emotion recognition,” IET Biometrics, doi: 10.1049/iet-bmt.2017.1060.
. A. Munir, A. Hussain, S. A. Khan, M. Nadeem, and S. Arshid, 92018) “ Illumination invariant facial expression recognition using selected merged binary patterns for real world images,” Optic 158: 1016-1025.
. M. J. Lyons, S. Akamatsu, M. Kamachi, J. Gyoba and J. Budynek (1998, April), “The Japanese female facial expression (JAFFE) database,” In Proceedings of third international conference on automatic face and gesture recognition (pp. 14-16).
. P. Viola, and M. Jones (2001, December). “Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001 (Vol 1, pp 1-1).
. P. Lucey., J.F. Cohn., T. Kanade., J. Saragih., Z. Ambadar., and I. Matthews (2010, June). “The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression.” In 2010 ieee computer society conference on computer vision and pattern recognition-workshops (pp. 94-101).
Downloads
Published
How to Cite
Issue
Section
License
Authors who submit papers with this journal agree to the following terms.
