Name
Title | ||
|---|---|---|
Facial Expression Classification Using Supervised Descent Method Combined With PCA and SVM. |
Abstract | ||
|---|---|---|
It has been well known that there is a correlation between facial expression and person's internal emotional state. In this paper we use an approach to distinguish between neutral and some other expression: based on the displacement of important facial points (coordinates of edges of the mouth, eyes, eyebrows, etc.). Further the feature vectors are formed by concatenating the landmarks data from Supervised Descent Method, applying PCA and use these data as an input to Support Vector Machine (SVM) classifier. The experimental results show improvement of the recognition rate in comparison to some state-of-the- art facial expression recognition techniques. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-13386-7_13 | BIOMETRIC AUTHENTICATION (BIOMET 2014) |
Keywords | Field | DocType |
Supervised Descent Method,SVM,PCA,Facial expression,Emotion recognition | Feature vector,Supervised descent method,Pattern recognition,Facial expression recognition,Biochemistry,Emotion recognition,Support vector machine,Chemistry,Correlation,Facial expression,Artificial intelligence,Classifier (linguistics) | Conference |
Volume | ISSN | Citations |
8897 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 17 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Agata Manolova | 1 | 13 | 7.84 |
| Nikolay Neshov | 2 | 0 | 1.69 |
| Stanislav Panev | 3 | 8 | 3.57 |
| Krasimir Tonchev | 4 | 10 | 8.51 |