2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 2 , Issue 1 , PP: 31-41, 2020 | Cite this article as | XML | Html | PDF | Full Length Article
Anjali Raghav 1 * , Monika Gupta 2
Doi: https://doi.org/10.54216/FPA.020104
Facial expressions are the translation of the emotions such as anger, sadness, happiness, disgust felt by a person. Facial expression recognition, classification of expressions which has application in various industries such as hospitality, medical to name a few. There are various datasets available for facial expression recognition, we used FER 2013 dataset to build a classification algorithm. This algorithm classifies the emotions into seven categories namely, angry, disgust, happy, sad, fear, surprise and neutral. In traditional convolutional neural network algorithm the computing time is very large, ensemble learning significantly reduced the computing time and offered a promising accuracy. Features of images were extracted using the convolutional neural network, further these features were implemented using XGBoost and Random Forest to build classification algorithms and an accuracy of 77% and 74% was obtained. This was comparable to the accuracy obtained by traditional convolutional neural network which was 75% also with very less computing time.
Ensemble Learning , Facial Expression Recognition
[1] S. Gilda, H. Zafar, C. Soni and K. Waghurdekar, "Smart music player integrating facial emotion recognition and music mood recommendation," 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai,2017,pp.154-158, DOI: 10.1109/WiSPNET.2017.8299738.
[2] Yamashita, R., Nishio, M., Do, R.K.G., Convolutional neural networks: an overview and application in radiology. Insights Imaging, Volume 9, 2018, pp. 611–629 , DOI: https://doi.org/10.1007/s13244-018-0639-9
[3] ZixiangFei, Erfu Yang, David Day-Uei Li, StephenButler, Winifred Ijomah, XiaLi, Huiyu Zhoue, Deep convolution network based emotion analysis towards mental health care, Neurocomputing, Volume 388, 2020, pp. 212-227. DOI:https://doi.org/10.1016/j.neucom.2020.01.034
[4] Muhammad Naveed Riaz, Yao Shen, Muhammad Sohail, Minyi Guo, eXnet: An Efficient Approach for Emotion Recognition in the Wild, Sensors (Basel), Volume 20, 2020, pp. 1087-1091. DOI: 10.3390/s20041087
[5] Megha Chandran, Dr. Naveen S , A Review on Facial Expression Recognition using Deep Learning, International Journal of Engineering Research and Technology, Volume 7,2020, Issue 13. DOI: https://www.ijert.org/a-review-on-facial-expression-recognition-using-deep-learning
[6] Jie Shao, Yong sheng Qian, Three convolutional neural network models for facial expression recognition in the wild, Neurocomputing, Volume 355, 2019, pp. 82-92. DOI: https://doi.org/10.1016/j.neucom.2019.05.005
[7] Jason C.Hung, Kuan-Cheng Lin, Nian-Xiang Lai, Recognizing learning emotion based on convolutional neural networks and transfer learning, Applied Soft Computing, Volume 84, 2019, pp. 105724. DOI: https://doi.org/10.1016/j.asoc.2019.105724
