This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License.
Shan, C., Gong, S., & McOwan, P. W. (2009). Facial expression recognition based on local binary patterns: a comprehensive study. Image and Vision Computing, 27(6), 803-816.Search in Google Scholar
V. Bakiasi and M. Muça, “Variational Autoencoder for Face Expression Classification”, in Thirtieth International Conference On: “Social And Natural Sciences – Global Challenge 2023”, pp. 101-110.Search in Google Scholar
I. T. Meftah, N. Le Thanh, and C. B. Amar, “Emotion recognition using KNN classification for user modeling and sharing of affect states,” in International Conference on Neural Information Processing, 2012, pp. 234-242.Search in Google Scholar
M. Abdulrahman and A. Eleyan, “Facial expression recognition using support vector machines,” in 2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015, pp. 276-279.Search in Google Scholar
Martinez, A. M. (2004). Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Transactions on pattern analysis and machine intelligence, 26(5), 673-685.Search in Google Scholar
Bartlett, M. S., Littlewort, G., Frank, M., Lainscsek, C., Fasel, I., & Movellan, J. (2005). Recognizing facial expression: Machine learning and application to spontaneous behavior. In IEEE International Conference on Systems, Man and Cybernetics (Vol. 4, pp. 3464-3469).Search in Google Scholar
Li, S. Z., & Jain, A. K. (2011). Handbook of face recognition. Springer Science & Business Media.Search in Google Scholar
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.Search in Google Scholar
Rish, I. (2001). An empirical study of the naive Bayes classifier. In IJCAI 2001 workshop on empirical methods in artificial intelligence (Vol. 3, No. 22, pp. 41-46).Search in Google Scholar
Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification (2nd ed.). Wiley-Interscience.Search in Google Scholar
H. Bhavsar and A. Ganatra, “A Comparative Study of Training Algorithms for Supervised Machine Learning”, International Journal of Soft Computing and Engineering (IJSCE), Vol. 2, Issue. 4, September 2012.Search in Google Scholar
Vapnik, V. (1995). The nature of statistical learning theory. Springer Science & Business Media.Search in Google Scholar
S.Archana and Dr. K.Elangovan, “Survey of Classification Techniques in Data Mining”, International Journal of Computer Science and Mobile Applications, Vol. 2 Issue. 2, February 2014.Search in Google Scholar
Sagar S. Nikam, “A Comparative Study of Classification Techniques in Data Mining Algorithms”, Oriental Journal of Computer Science & Technology, Vol. 8, April 2015.Search in Google Scholar
C. Y. Yong, R. Sudirman, and K. M. Chew, “Facial expression monitoring system using pca-bayes classifier,” in Future Computer Sciences and Application (ICFCSA), 2011 International Conference on, 2011, pp. 187-191.Search in Google Scholar
I. M. Revina and W. S. Emmanuel, “A survey on human face expression recognition techniques,” Journal of King Saud University-Computer and Information Sciences, 2018.Search in Google Scholar