Recognition and Classification of human reactions with facial features
DOI:
https://doi.org/10.61841/0t7nxc91Keywords:
(Principal component analysis) PCA,, Machine learning,, Emotion recognition,, (Support Vector Machine) SVM,, ExpressionsAbstract
This paper compares different algorithms to identify the different emotions from facial expressions. This presents about recognition of 6 basic expressions like Sad, Happy, Disgust, Fear, Surprise, Anger, and Neutral. This paper does a review about various algorithms that effectively recognizes facial expressions with emotions. A survey of database and algorithms were explained along with a partly implementation using Principal Component Analysis (PCA). By this survey it is observed that say Support Vector Machine (SVM) and PCA are efficient than other algorithms. An implementation on Principal component analysis is carried out in this paper to identify the emotions using MATLAB.
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1. Shenglan Liu, ShuaiGuo, Hong Qiao, Yang Wang, Bin Wang, Wenbo Luo, Mingming Zhang, Keye Zhang, Bixuan Du. Multi-view Laplacian Eigenmaps Based on Bag-of-Neighbors For RGB-D Human Emotion Recognition Article in Information Sciences 509 · September 2019.
2. HiviIsmat Dino, Iraq MaiwanBahjatAbdulrazzaq. Facial Expression Classification Based on SVM, KNN and MLP Classifiers. Computer Science Published in International Conference on… 2018.
3. Deepak Kumar Jain. Extended Deep Neural Network for Facial Emotion Recognition. Article in Pattern Recognition Letters 120 · April 2019.
4. Capriola-Hall, Youssef A, Abbott AL. Differences in Facial Emotion Expression in Autism: Evidence for the Utility of Machine Classification. Article in Behavior Therapy · December 2019.
5. Jingying Chen, YongqiangLv, author Can Xu .Automatic social signal analysis: Facial expression recognition using difference convolution neural network.Computer Science Published in J. Parallel Distrib. Comput. 2019.
6. Bharat Richhariya, DeepakGupta, Facial expression recognition using iterative universum twin support vector machine. Applied Soft Computing .2019.
7. Anurag Deahis, NITAgartala, AshimSaha, NITAgartala, A Human Facial Expression Recognition Model Based on Eigen Face Approach.Computer science 2015.
8. MdFazlay Rabbi MasumBillah, Muhammad Abdullah Adnan, SMARTLET: A Dynamic Architecture for Real Time Face Recognition in Smartphone Using Cloudlets and CloudArticle · July 2020.
9. J.A.Domínguez-Jiménez, K.C.Campo-Landines, J.C.Martínez-Santos, E.J.Delahoz, S.H.Contreras-Ortiz, A machine learning model for emotion recognition from physiological. 2019.
10. Michael E. Mavroforakis, SergiosTheodoridis, Support Vector Machine (SVM) classification through geometry.Signal Processing Conference (EUSIPCO), European. 2015.
11. Tara N. Sainath, IBM T. J. Watson Research Center, Deep convolutional neural networks for LVCSR. IEEE International Conference on Acoustics, Speech and Signal Processing. 2013.
12. Rikiya Yamashita, Mizuho Nishio, Kaori Togashi. Convolutional neural networks: an overview and application in radiology.
13. Shweta Taneja,DharnaGureja,CharuGupta An Enhanced K-Nearest Neighbor Algorithm Using Information Gain and Clustering. 2014.
14. P Ganesan, V. Rajini, B.S. Sathish, KhamarBashaShaik, India.HSVcolor space based segmentation of region of interest in satellite images.International 2014.
15. Qian Li, Usman Niaz, Bernard Merialdo An improved algorithm on Viola-Jones object detector.10th International Workshop on Content-Based Multimedia Indexing (CBMI).2012.
16. Wen-yao LU, Ming YANG. Face Detection Based on Viola-Jones Algorithm Applying Composite Features. International Conference on Robots & Intelligent System (ICRIS).2019.
17. B. G. D. A. Madhusanka, T. M. W. Vithanawasam. Face and Upper-Body Emotion Recognition Using Service Robot’s Eyes in a Domestic Environment, 2019.
18. Ibrahim A. Adeyanju, Elijah O. Omidiora. Performance evaluation of different support vector machine kernels for face emotion recognition. SAI Intelligent Systems Conference. 2015.
19. P. Swarnalatha, Hari Prasad Mal, Facial expression detection using facial expression model. 2017.
20. SukanyaSagarikaMeher, Pallavi Maben. Face recognition and facial expression identification using PCA.IEEE International Advance Computing Conference 2014.
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