scholarly journals Learning Directional LBP Features and Discriminative Feature Regions for Facial Expression Recognition

2017 ◽  
Vol 20 (5) ◽  
pp. 748-757 ◽  
Author(s):  
Hyunwoo Kang ◽  
Kil-Taek Lim ◽  
Chulho Won
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Discriminative Feature

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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