Facial Expression Recognition Based on 2D Gabor Transforms and SVM

2011 ◽  
Vol 58-60 ◽  
pp. 238-242
Author(s):  
Chun Hui Liu ◽  
Zhao Zheng ◽  
Feng Gao
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Experimental Result ◽  
Expression Recognition ◽  
Facial Region ◽  
The Mean ◽  
Class C

In the facial expression recognition, a dimension disaster will arise when taking the coefficient of Gabor transforms as the expression eigenvectors. To avoid this issue we draw grids on facial region, making the mean coefficient value of Gabor transforms of each gird as the eigenvectors. Furthermore we classify the expression by constructing the multi-class C-SVC, improved the accuracy and speed of the algorithm by dropping the redundant features using sequential backward selection. The experimental result proves the superiority of the algorithm we proposed to other algorithms.

scholarly journals Subject Independent Automatic Facial Expression Recognition from Partially Occluded Still Images by Employing Appearance Based Feature Extraction Schemes

2019 ◽  
Vol 8 (10) ◽  
pp. 3422-3428
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Scenario Development ◽  
Still Images ◽  
Facial Region ◽  
Recognition Of Facial Expressions ◽  
Holistic Approaches ◽  
Facial Area ◽  
Partially Occluded

Facial expression recognition is the process of identifying human emotion through expressions. The world around us is constantly changing and in this ever-changing scenario, development of a system which performs automatic detection and recognition of facial expressions in a given scene is of paramount importance, and at the same time highly challenging. It becomes even more challenging when the aforesaid scene is partially occluded, thus limiting the facial area which could be explored. As such, expression recognition from partially occluded images is still largely an unexplored area. The proposed work is an attempt towards subject independent automatic expression recognition from partially occluded images. Salient features of the proposed work involves careful approximation of contributions made by facial regions like eye, mouth, and nose towards recognition of each basic expression; determination of a particular region in face which contributes the most discriminative and abstract feature for recognition of a particular expression; identification of facial region wherein expression recognition is independent of any occlusion happening with respect to that particular region. The proposed work to begin with segments facial regions from a static facial image; discriminative and abstract features extracted from so segmented facial regions are experimented upon to better understand the contribution made by each region in recognition of a facial expression. Various appearance features such as HOG, LBP and OGBP have been incorporated in the experimentation, and results obtained thereby infer that mouth region convey lion’s share of the information about probabilistic determinant of an expression and its intensity when compared to remaining regions. The proposed system outperforms holistic approaches in connection with facial expression recognition.

scholarly journals Facial Expression Recognition System

2020 ◽  
pp. 533-536
Keyword(s):  
Image Processing ◽  
Facial Expression ◽  
Machine Vision ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Processing Method ◽  
Experimental Result ◽  
Expression Recognition

Emotion recognition is a prominent tough problem in machine vision systems. The significant way humans show emotions is through facial expressions. In this paper we used a 2D image processing method to recognize the facial expression by extracting of features. The proposed algorithm passes through few preprocessing steps initially. And then the preprocessed image is partitioned into two main parts Eyes and Mouth. To identify the emotions Bezier curves are drawn for main parts. The experimental result shows that the proposed technique is 80% to 85% accurate.

scholarly journals Facial Expression Recognition Based on Facial Region Segmentation and Modal Value Approach

2014 ◽  
Vol E97.D (4) ◽  
pp. 928-935 ◽  
Author(s):  
Gibran BENITEZ-GARCIA ◽  
Gabriel SANCHEZ-PEREZ ◽  
Hector PEREZ-MEANA ◽  
Keita TAKAHASHI ◽  
Masahide KANEKO
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Region Segmentation ◽  
Facial Region
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