scholarly journals Genetic Programming-Based Feature Learning for Facial Expression Classification

2020 ◽  
Author(s):  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Facial Expression ◽  
Genetic Programming ◽  
Feature Learning ◽  
Facial Expression Classification ◽  
Expression Classification

scholarly journals Genetic Programming-Based Feature Learning for Facial Expression Classification

2021 ◽  
Author(s):  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Facial Expression ◽  
Genetic Programming ◽  
Feature Learning ◽  
Data Sets ◽  
Training Images ◽  
High Classification Accuracy ◽  
The Face ◽  
Almost All ◽  
Facial Expression Classification ◽  
Expression Classification

© 2020 IEEE. Facia1 expression classification is an important but challenging task in artificial intelligence and computer vision. To effectively solve facial expression classification, it is necessary to detect/locate the face and extract features from the face. However, these two tasks are often conducted separately and manually in a traditional facial expression classification system. Genetic programming (GP) can automatically evolve solutions for a task without rich human intervention. However, very few GP-based methods have been specifically developed for facial expression classification. Therefore, this paper proposes a GP-based feature learning approach to facial expression classification. The proposed approach can automatically select small regions of a face and extract appearance features from the small regions. The experimental results on four different facial expression classification data sets show that the proposed approach achieves significantly better results in almost all the comparisons. To further show the effectiveness of the proposed approach, different numbers of training images are used in the experiments. The results indicate that the proposed approach achieves significantly better performance than any of the baseline methods using a small number of training images. Further analysis shows that the proposed approach not only selects informative regions of the face but also finds a good combination of various features to obtain a high classification accuracy.

scholarly journals Genetic Programming-Based Feature Learning for Facial Expression Classification

2021 ◽  
Author(s):  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Facial Expression ◽  
Genetic Programming ◽  
Feature Learning ◽  
Data Sets ◽  
Training Images ◽  
High Classification Accuracy ◽  
The Face ◽  
Almost All ◽  
Facial Expression Classification ◽  
Expression Classification

© 2020 IEEE. Facia1 expression classification is an important but challenging task in artificial intelligence and computer vision. To effectively solve facial expression classification, it is necessary to detect/locate the face and extract features from the face. However, these two tasks are often conducted separately and manually in a traditional facial expression classification system. Genetic programming (GP) can automatically evolve solutions for a task without rich human intervention. However, very few GP-based methods have been specifically developed for facial expression classification. Therefore, this paper proposes a GP-based feature learning approach to facial expression classification. The proposed approach can automatically select small regions of a face and extract appearance features from the small regions. The experimental results on four different facial expression classification data sets show that the proposed approach achieves significantly better results in almost all the comparisons. To further show the effectiveness of the proposed approach, different numbers of training images are used in the experiments. The results indicate that the proposed approach achieves significantly better performance than any of the baseline methods using a small number of training images. Further analysis shows that the proposed approach not only selects informative regions of the face but also finds a good combination of various features to obtain a high classification accuracy.

From Face to Facial Expression

2010 ◽  
pp. 217-238
Author(s):  
Zakia Hammal ◽  
Zakia Hammal
Keyword(s):  
Computer Vision ◽  
Facial Expression ◽  
Everyday Life ◽  
Facial Expression Recognition ◽  
Multidisciplinary Approach ◽  
Expression Recognition ◽  
Future Challenges ◽  
Facial Expression Classification ◽  
Expression Classification ◽  
Processing Steps

This chapter addresses recent advances in computer vision for facial expression classification. The authors present the different processing steps of the problem of automatic facial expression recognition. They describe the advances of each stage of the problem and review the future challenges towards the application of such systems to everyday life situations. The authors also introduce the importance of taking advantage of the human strategy by reviewing advances of research in psychology towards multidisciplinary approach for facial expression classification. Finally, the authors describe one contribution which aims at dealing with some of the discussed challenges.

Automated sheep facial expression classification using deep transfer learning

2020 ◽  
Vol 175 ◽  
pp. 105528
Author(s):  
Alam Noor ◽  
Yaqin Zhao ◽  
Anis Koubaa ◽  
Longwen Wu ◽  
Rahim Khan ◽  
...  
Keyword(s):  
Facial Expression ◽  
Transfer Learning ◽  
Facial Expression Classification ◽  
Expression Classification

scholarly journals Wink based facial expression classification using machine learning approach

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Mamunur Rashid ◽  
Norizam Sulaiman ◽  
Mahfuzah Mustafa ◽  
Bifta Sama Bari ◽  
Md Golam Sadeque ◽  
...  
Keyword(s):  
Machine Learning ◽  
Facial Expression ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Facial Expression Classification ◽  
Expression Classification

A Design of Face Detection and Facial Expression Recognition Techniques Based on Boosting Schema

2011 ◽  
Vol 121-126 ◽  
pp. 617-621 ◽  
Author(s):  
Chang Yi Kao ◽  
Chin Shyurng Fahn
Keyword(s):  
Facial Expression ◽  
Face Detection ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Extraction Procedure ◽  
Expression Recognition ◽  
Classification Procedure ◽  
Facial Expression Classification ◽  
Human Faces ◽  
Expression Classification

During the development of the facial expression classification procedure, we evaluate three machine learning methods. We combine ABAs with CARTs, which selects weak classifiers and integrates them into a strong classifier automatically. We have presented a highly automatic facial expression recognition system in which a face detection procedure is first able to detect and locate human faces in image sequences acquired in real environments. We need not label or choose characteristic blocks in advance. In the face detection procedure, some geometrical properties are applied to eliminate the skin color regions that do not belong to human faces. In the facial feature extraction procedure, we only perform both the binarization and edge detection operations on the proper ranges of eyes, mouth, and eyebrows to obtain the 16 landmarks of a human face to further produce 16 characteristic distances which represent a kind of expressions. We realize a facial expression classification procedure by employing an ABA to recognize six kinds of expressions. The performance of the system is very satisfactory; whose recognition rate achieves more than 90%.

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