Facial Expression Recognition Based on Multiple Feature Fusion in Video

2018 ◽  
Author(s):  
Jiahui Fan ◽  
Yun Tie ◽  
Lin Qi
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Feature Fusion ◽  
Expression Recognition ◽  
Multiple Feature ◽  
Multiple Feature Fusion

scholarly journals Facial Expression Recognition in Video with Multiple Feature Fusion

2019 ◽  
Vol 8 (6S) ◽  
pp. 362-365
Keyword(s):  
Facial Expression Recognition ◽  
Feature Fusion ◽  
Expression Recognition ◽  
Outward Appearance ◽  
Course Of Action ◽  
Multiple Feature ◽  
Dynamic Surfaces ◽  
Curve Change ◽  
Multiple Feature Fusion ◽  
Free Dynamic

Video based outward appearance affirmation has been a long standing issue and pulled in creating thought starting late. The way to a productive outward appearance affirmation system is to manhandle the conceivable outcomes of shifting media modalities and structure incredible highlights to effectively depict the facial appearance and course of action changes achieved by facial developments. We propose a powerful system to address this issue in this paper. In our examination, both visual modalities (face pictures) and sound modalities (talk) are used. Another part descriptor known as bar chart of familiarized Gradients from 3 Orthogonal Planes (HOG-TOP) is projected to free dynamic surfaces from video courses of action to depict facial look changes. In addition, another effective geometric element got from the curve change of facial places of interest is proposed to get facial plan changes.

scholarly journals Recognition of Facial Expressions under Varying Conditions Using Dual-Feature Fusion

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Awais Mahmood ◽  
Shariq Hussain ◽  
Khalid Iqbal ◽  
Wail S. Elkilani
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Feature Fusion ◽  
Expression Recognition ◽  
Human Beings ◽  
Recognition Of Facial Expressions ◽  
Discriminative Feature ◽  
High Recognition Accuracy ◽  
Facial Images ◽  
New Framework

Facial expression recognition plays an important role in communicating the emotions and intentions of human beings. Facial expression recognition in uncontrolled environment is more difficult as compared to that in controlled environment due to change in occlusion, illumination, and noise. In this paper, we present a new framework for effective facial expression recognition from real-time facial images. Unlike other methods which spend much time by dividing the image into blocks or whole face image, our method extracts the discriminative feature from salient face regions and then combine with texture and orientation features for better representation. Furthermore, we reduce the data dimension by selecting the highly discriminative features. The proposed framework is capable of providing high recognition accuracy rate even in the presence of occlusions, illumination, and noise. To show the robustness of the proposed framework, we used three publicly available challenging datasets. The experimental results show that the performance of the proposed framework is better than existing techniques, which indicate the considerable potential of combining geometric features with appearance-based features.

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