Name
Title | ||
|---|---|---|
AFFECTIVE STATE RECOGNITION BASED ON EYE GAZE ANALYSIS USING TWO–STREAM CONVOLUTIONAL NETWORKS |
Abstract | ||
|---|---|---|
In this paper, we propose a novel technique that combines the concept of spatially targeted optical flow with image processing, for affect state recognition, concerning a wide variety of learner types, including children with autism and mainstream children. We exploit the advantages of deep Neural Networks on image classification, by adopting a two-stream CNN approach for the recognition task, based on gaze analysis. As there is not a publicly available dataset to contain such a variety of learner types, a dataset was created in order to evaluate the performance of our algorithm. We validate our approach using this dataset, by optimising a mean-square error loss function, obtaining promising results for this challenging task. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/MLSP.2018.8517010 | 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP) |
Keywords | Field | DocType |
Affective computing,Convolutional Neural Networks,gaze analysis | Gaze,Pattern recognition,Computer science,Convolutional neural network,Image processing,Exploit,Eye tracking,Artificial intelligence,Affective computing,Contextual image classification,Optical flow,Machine learning | Conference |
ISSN | ISBN | Citations |
1551-2541 | 978-1-5386-5478-1 | 0 |
PageRank | References | Authors |
0.34 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christina Chrysouli | 1 | 11 | 2.25 |
| Nicholas Vretos | 2 | 33 | 12.21 |
| Petros Daras | 3 | 1129 | 131.72 |