Comparison Between Facial Expression Recognition Algorithms - For Effective Method
DOI:
https://doi.org/10.32628/CSEIT206612Keywords:
Optimization, Eigen face, Fisher face, LBPH, DNN & CNN.Abstract
Facial expression is an ancient element for identifying humans. Human behavior, thinking or mood can be easily understood through facial expression. At present, facial expression can be evaluated by algorithm based on facial expression AI. In this paper, comparative studies have been done in methods related to facial recognition and an attempt has been made to evaluate it. Previous and recent research paper has been investigated to find out the related effective method.
References
- Rishabh Bhardway etal,2015, A Review on the use of facial expression for emotion Recognition, IJSRMS, Volume 3, Issue 4.
- Shiliange sun etal, 2019, A survey of Optimization Methods from a Machine Learning Perspective.
- Rochak Agrawal, 2019/ http:// medium com / analysis-vidhya / optimization -algorithms – algorithms – for –deep – learning – 1fLa2bd- 4c-46b.
- Yogish Naik, May 2014, Detailed Survey of Different Face Recognition Approaches, ISSN, Volume 3, Issue 5.
- Sushma Jaiswal etal, 2011, Comparison Between Face Recognition Algorithm – Eigenface, Fisherfaces and Elastic Bunch Graph Matching, ISSN, Volume2, No 7.
- Priyanka Dharani and Dr. AS Vibhute, May 2017, Face recognition using wavelet Neural Network, ISSN, Volume 7, Issue 5.
- Nivedita Chitra and Geeta Nijhawan, Jun 2016, Facial Expression Recognition Using Local Binary Pattern and Support Vector Machine, ISSN, Volume 3, Issue 6.
- Ankit Jain etal, May 2019, An Emotion Recognition Framework Through Local Binary Patterns, ISSN, Volume 6, Issue 5.
- Reshma MS and Sabeena M, A Servey on Facial Image Recognition and Reconstraction, ISSN, Volume 3.
- Vivienne size seniour etal, Efficient Processing of Deep Neural Networks: A Tutorial and Survey, IEEE.
- Malyala Divya etal, November 2019, Effective Facial Emotion Recognition using Convolutional Neural Network Algorithm, IJRIE, Volume 2, Issue 4.
- S.D. Lalita and K.K. Thyagharajan, October 2019, Micro – Facial Expression Recognition in Video Based on Optimal Convolutional Neural Network (MFEOCNN) Algorithm, (IJEAI) ISSN, Volume 9, Issue 1.
- Nivedita Chitra and Geeta Nijhawan, June 2016, Facial Expression Recognition Using Local Binary Pattern and Support Vector Machine, ISSN, Volume 3, Issue 6.
- https://iq.opengenus.org/lbph-algorithm-for-face-recognition/
- https://medium.com/analytics-vidhya/optimization-algorithms-for-deep-learning-1f1a2bd4c46b
- https://analyticsindiamag.com/deep-learning-image-classification-with-cnn-an-overview
- Anil kumar N Bhatt, March 2020, Deep Learning Image Classification With CNN – An Overview, Articale.
- Sonali Singh, 2020, Emotion Evaluation from Facial Expression using AI Techniques: A Review, BSSS Journal of Computer, Volume 12, Issue 1.
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