Facial Expression Recognition Using Machine Learning Techniques

2021 ◽  
Author(s):  
Salam Ullah ◽  
Atif Jan ◽  
Gul Muhammad Khan
Keyword(s):  
Machine Learning ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Machine Learning Techniques ◽  
Expression Recognition ◽  
Learning Techniques

Image Processing and Machine Learning Techniques for Facial Expression Recognition

2009 ◽  
pp. 247-262 ◽  
Author(s):  
Anastasios Koutlas ◽  
Dimitrios I. Fotiadis
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Machine Learning Techniques ◽  
Automatic Identification ◽  
Expression Recognition ◽  
Basic Emotions ◽  
Learning Techniques ◽  
The Face

The aim of this chapter is to analyze the recent advances in image processing and machine learning techniques with respect to facial expression recognition. A comprehensive review of recently proposed methods is provided along with an analysis of the advantages and the shortcomings of existing systems. Moreover, an example for the automatic identification of basic emotions is presented: Active Shape Models are used to identify prominent features of the face; Gabor filters are used to represent facial geometry at selected locations of fiducial points and Artificial Neural Networks are used for the classification into the basic emotions (anger, surprise, fear, happiness, sadness, disgust, neutral); and finally, the future trends towards automatic facial expression recognition are described.

Facial Expression Recognition with Gender Identification

2020 ◽  
pp. 57-63
Author(s):  
admin admin ◽  
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...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Human Face ◽  
Gender Identification ◽  
Artificial Intelligence Techniques ◽  
The Mind

The human facial emotions recognition has attracted interest in the field of Artificial Intelligence. The emotions on a human face depicts what’s going on inside the mind. Facial expression recognition is the part of Facial recognition which is gaining more importance and need for it increases tremendously. Though there are methods to identify expressions using machine learning and Artificial Intelligence techniques, this work attempts to use convolution neural networks to recognize expressions and classify the expressions into 6 emotions categories. Various datasets are investigated and explored for training expression recognition models are explained in this paper and the models which are used in this paper are VGG 19 and RESSNET 18. We included facial emotional recognition with gender identification also. In this project we have used fer2013 and ck+ dataset and ultimately achieved 73% and 94% around accuracies respectively.

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