Name
Title | ||
|---|---|---|
Research on Facial Expression Recognition Technology Based on Convolutional-Neural-Network Structure |
Abstract | ||
|---|---|---|
AbstractHuman facial expressions change so subtly that recognition accuracy of most traditional approaches largely depend on feature extraction. In this article, the authors employ a deep convolutional neural network CNN to devise a facial expression recognition system to discover deeper feature representation of facial expression. The proposed system is composed of the input module, the pre-processing module, the recognition module and the output module. The authors introduce jaffe and ck+ to simulate and evaluate the performance under the influence of different factors e.g. network structure, learning rate and pre-processing. The authors also examine the anti-noise property of the system with zero-mean gaussian white noise. In addition, they simulate the recognition accuracy on different expression pairs and discuss the confusion issue on similar expression recognition. Finally, they introduce the k-nearest neighbor KNN algorithm compared with CNN to make the results more convincing. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.4018/IJSI.2018100108 | Periodicals |
Keywords | DocType | Volume |
Affective Computing, Artificial Intelligence, Computer Vision, Convolutional Neural Network, Deep Learning, Facial Expression Recognition Research, Machine Learning | Journal | 6 |
Issue | ISSN | Citations |
4 | 2166-7160 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junqi Guo | 1 | 61 | 15.07 |
| Ke Shan | 2 | 0 | 0.68 |
| Hao Wu | 3 | 143 | 18.69 |
| Rongfang Bie | 4 | 547 | 68.23 |
| Wenwan You | 5 | 1 | 1.06 |
| Di Lu | 6 | 1 | 1.38 |