In recent years, facial expressions recognition has automatically attracted attention in car vision and non-linguistic
communications due to its extremely important applications. The reason of the popularity is extensive applications in various fields. As
a result, the implementation of Facial expressions recognition system is very important. For this purpose, various methods such as
neural network, neural-Fuzzy network, fuzzy algorithms, deep learning, etc can be used. Various simulations provide different accuracy
of facial expressions recognition. In this paper, different ways to identify facial expressions recognition are examined and compared in
two ways, quantitative (accuracy of each method) and qualitative (statistical society, advantages, disadvantages, goals and obtained
results from the method). This study can help researchers choose the best method for their research and can be familiar with the
advantages and disadvantages in each method.
Published In:IJCSN Journal Volume 6, Issue 5
Date of Publication : October2017
Pages : 599-614
Tables : 05
Mahsa Naeeni Davarani : Department of Computer Engineering, Islamic Azad University,
Central Tehran Branch, Tehran, Iran.
Touraj Banirostam : Department of Computer Engineering, Islamic Azad University,
Central Tehran Branch, Tehran, Iran.
Hayedeh Saberi : Department of Psychology, Islamic Azad University,
Roudehen Branch, Roudehen, Iran.
Facial expressions recognition, image based methods, feature-based methods, EEG-based methods
In this study, different techniques of face recognition and
feature extraction are discussed. Pattern based methods
are implemented easily but don't provide general structure
of face. Appearance based methods show optimal feature
points that can indicate general structure of face.
Geometry based methods such as face feature extraction
present stable features.
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