Review on Smile Detection

Authors

  • Anurag Goswami  School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India
  • Ganjigunta Ramakrishna  School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India
  • Dr. Rajni Sethi  School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India

DOI:

https://doi.org/10.32628/CSEIT2172134

Keywords:

Smile Detection, Adaboost, GENKI4K, CNN

Abstract

Facial expressions are a result of specific movement of face muscles, and these face expressions are considered as a visible sign of a person’s internal thought process, intensions, and internal emotional states. Smile is such a face expression which often indicates, satisfaction, agreement, happiness, etc. Though, a lot of studies have been done over detection of Facial Expression in last decade, smile detection had attracted researcher for more deeper studies. In this review paper, different type of available smile detection so far has been discussed such as Deep Convolutional Neural Network (CNN), Hidden Marcov Model(HMM), K-Nearest Neighbours(KNN), Self Similarity of Gradient(GSS), Histogram of Oriented Gradients (HOG), Gabor-Energy Filters and Local Binary Pattern(LBP) etc and classifier like HAAR Classifier, Hidden Markov Model(HMM), Adaboost Support Vector Machine (SVM),Softmax Classifier and Extreme Learning Machine(ELM).This review paper will prove beneficial for learning about smile detection and its application.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-04-30

Issue

Volume 7, Issue 2, March-April-2021

Section

Research Articles

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How to Cite

[1]
Anurag Goswami, Ganjigunta Ramakrishna, Dr. Rajni Sethi, " Review on Smile Detection " International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 2, pp.577-583, March-April-2021. Available at doi : https://doi.org/10.32628/CSEIT2172134