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A Review On Various Techniques To Recognize Gesture Based Facial Expressions


Affiliations
1 Department of Computer Science, Central University of Jammu, Jammu, India
2 Department Computer Science, Central University of Jammu, Jammu, India
 

Nowadays, the Facial Expression Recognition (FER) system is a very important research topic in the fields of pattern recognition, Human-Computer Interaction (HCI) and Artificial Intelligence. The most essential steps in FER system are facial feature extraction and classification of expressions which improves the system performance for human computer interaction. This article reviews the feature extraction approaches such as Local Binary Pattern (LBP) (Divide the examined window into cells (e.g. 16x16 pixels for each cell).For each pixel in a cell, compare the pixel to each of its 8 neighbors (on its left-top, left-middle, left-bottom, right-top, etc.). Follow the pixels along a circle, i.e. clockwise or counter-clockwise. Where the center pixel's value is greater than the neighbor's value, write "0". Otherwise, write "1". This gives an 8-digit binary number (which is usually converted to decimal for convenience).Compute the histogram, over the cell, of the frequency of each "number" occurring (i.e., each combination of which pixels are smaller and which are greater than the center). This histogram can be seen as a 256-dimensional feature vector. Optionally normalize the histogram. Concatenate (normalized) histograms of all cells. This gives a feature vector for the entire window.), Spatio-Temporal feature using Local Zernike moment and Local Directional Position Pattern (LDPP). Also, the various combinations of Convolutional Neural Network (CNN) and Deep Neural Network (DNN) classifiers are reviewed. In addition, this paper makes a comparative study of various techniques used for analysing gesture based facial expressions and their corresponding performance.

Keywords

Facial Expression Recognition (FER), Convolutional Neural Network (CNN), Deep Neural Network (DNN), Local Binary Pattern (LBP), Feature Extraction.
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  • A Review On Various Techniques To Recognize Gesture Based Facial Expressions

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Authors

Rohini Mahajan
Department of Computer Science, Central University of Jammu, Jammu, India
Devanand Padha
Department Computer Science, Central University of Jammu, Jammu, India

Abstract


Nowadays, the Facial Expression Recognition (FER) system is a very important research topic in the fields of pattern recognition, Human-Computer Interaction (HCI) and Artificial Intelligence. The most essential steps in FER system are facial feature extraction and classification of expressions which improves the system performance for human computer interaction. This article reviews the feature extraction approaches such as Local Binary Pattern (LBP) (Divide the examined window into cells (e.g. 16x16 pixels for each cell).For each pixel in a cell, compare the pixel to each of its 8 neighbors (on its left-top, left-middle, left-bottom, right-top, etc.). Follow the pixels along a circle, i.e. clockwise or counter-clockwise. Where the center pixel's value is greater than the neighbor's value, write "0". Otherwise, write "1". This gives an 8-digit binary number (which is usually converted to decimal for convenience).Compute the histogram, over the cell, of the frequency of each "number" occurring (i.e., each combination of which pixels are smaller and which are greater than the center). This histogram can be seen as a 256-dimensional feature vector. Optionally normalize the histogram. Concatenate (normalized) histograms of all cells. This gives a feature vector for the entire window.), Spatio-Temporal feature using Local Zernike moment and Local Directional Position Pattern (LDPP). Also, the various combinations of Convolutional Neural Network (CNN) and Deep Neural Network (DNN) classifiers are reviewed. In addition, this paper makes a comparative study of various techniques used for analysing gesture based facial expressions and their corresponding performance.

Keywords


Facial Expression Recognition (FER), Convolutional Neural Network (CNN), Deep Neural Network (DNN), Local Binary Pattern (LBP), Feature Extraction.

References