Automatic Facial Expression Recognition Using Gabor Filter and Expression Analysis

2010 ◽  
Author(s):  
Jun Ou ◽  
Xiao-Bo Bai ◽  
Yun Pei ◽  
Liang Ma ◽  
Wei Liu
Keyword(s):  
Facial Expression ◽  
Expression Analysis ◽  
Facial Expression Recognition ◽  
Gabor Filter ◽  
Expression Recognition

scholarly journals Facial Expression Analysis using Convolutional Neural Networks

2020 ◽  
Vol 9 (4) ◽  
pp. 1142-1146
Keyword(s):  
Neural Networks ◽  
Facial Expression ◽  
Real Time ◽  
Facial Expressions ◽  
Convolutional Neural Networks ◽  
Expression Analysis ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Real Time System ◽  
Facial Expression Analysis

Human feelings are mental conditions of sentiments that emerge immediately as opposed to cognitive exertion. Some of the basic feelings are happy, angry, neutral, sad and surprise. These internal feelings of a person are reflected on the face as Facial Expressions. This paper presents a novel methodology for Facial Expression Analysis which will aid to develop a facial expression recognition system. This system can be used in real time to classify five basic emotions. The recognition of facial expressions is important because of its applications in many domains such as artificial intelligence, security and robotics. Many different approaches can be used to overcome the problems of Facial Expression Recognition (FER) but the best suited technique for automated FER is Convolutional Neural Networks(CNN). Thus, a novel CNN architecture is proposed and a combination of multiple datasets such as FER2013, FER+, JAFFE and CK+ is used for training and testing. This helps to improve the accuracy and develop a robust real time system. The proposed methodology confers quite good results and the obtained accuracy may give encouragement and offer support to researchers to build better models for Automated Facial Expression Recognition systems.

scholarly journals Three Level Optimization Models of Scaled Gabor Features for Facial Expression Recognition

2018 ◽  
Vol 7 (2.24) ◽  
pp. 348
Author(s):  
Neha . ◽  
Pratistha Mathur
Keyword(s):  
Computer Vision ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Gabor Filter ◽  
Recognition Rate ◽  
Feature Reduction ◽  
Expression Recognition ◽  
Texture Pattern ◽  
Average Recognition Rate ◽  
Expression Classification

The area of computer vision and machine learning for pattern recognition has witnessed the need for research for the development of algorithms for different applications such as human-computer interaction, automated access control and surveillance. In the field of computer vision Facial Expression Recognition has attracted the researcher’s interest. This paper presents a novel feature extraction technique: Gabor-Average-DWT-DCT for automatic facial expression recognition from a person's face image invariant of illumination. Facial Emotions have different edge and texture pattern. Gabor filter is able to extract edges and texture pattern of faces but with problem of huge dimension and high redundancy. The problem of huge dimension and high redundancy is reduced by proposed Average-DWT-DCT feature reduction technique in order to increase accuracy of system. Proposed Gabor- Average -DWT-DCT provides a compact feature vector for reducing response time of system compared to existing Gabor based expression classification. Detailed quantitative analysis is done and results that the average recognition rate of proposed technique is better than state of art results.  

scholarly journals RECOGNIZE HAPPY AND SADNESS EMOTIONS USING MUTUAL INFORMATION AND SUPPORT VECTOR CLASSIFICATION METHODS

2021 ◽  
Vol 03 (02) ◽  
pp. 204-208
Author(s):  
Ielaf O. Abdul-Majjed DAHL
Keyword(s):  
Mutual Information ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Gabor Filter ◽  
Current System ◽  
Support Vector ◽  
Expression Recognition ◽  
Support Vector Classifier ◽  
The Individual

In the past decade, the field of facial expression recognition has attracted the attention of scientists who play an important role in enhancing interaction between human and computers. The issue of facial expression recognition is not a simple matter of machine learning, because expression of the individual differs from one person to another based on the various contexts, backgrounds and lighting. The goal of the current system was to achieve the highest rate for two facial expressions ("happy" and "sad") The objective of the current work was to attain the highest rate in classification with computer vision algorithms for two facial expressions ("happy" and "sad"). This was accomplished through several phases started from image pre-processing to the Gabor filter extraction, which was then used for the extraction of important characteristics with mutual information. The expression was finally recognized by a support vector classifier. Cohn-Kanade database and JAFFE data base have been trained and checked. The rates achieved by the qualified data package were 81.09% and 92.85% respectively.

Sign in / Sign up

Export Citation Format

Share Document