Combining multiple kernel methods on Riemannian manifold for emotion recognition in the wild
In this paper, we present the method for our submission to the Emotion Recognition in the Wild Challenge (EmotiW 2014). The challenge is to automatically classify the emotions acted by human subjects in video clips under realworld environment. In our method, each video clip can be represented by thr...
Saved in:
Main Authors: | , , , , , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2014
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/6387 https://ink.library.smu.edu.sg/context/sis_research/article/7390/viewcontent/Combining_multiple_kernel_methods_on_riemannian_manifold_for_emotion_recognition_in_the_wild.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.sis_research-7390 |
---|---|
record_format |
dspace |
spelling |
sg-smu-ink.sis_research-73902021-11-23T02:38:27Z Combining multiple kernel methods on Riemannian manifold for emotion recognition in the wild LIU, M. WANG, R. LI, S. SHAN, S. HUANG, Zhiwu CHEN, X. In this paper, we present the method for our submission to the Emotion Recognition in the Wild Challenge (EmotiW 2014). The challenge is to automatically classify the emotions acted by human subjects in video clips under realworld environment. In our method, each video clip can be represented by three types of image set models (i.e. linear subspace, covariance matrix, and Gaussian distribution) respectively, which can all be viewed as points residing on some Riemannian manifolds. Then different Riemannian kernels are employed on these set models correspondingly for similarity/distance measurement. For classification, three types of classifiers, i.e. kernel SVM, logistic regression, and partial least squares, are investigated for comparisons. Finally, an optimal fusion of classifiers learned from different kernels and different modalities (video and audio) is conducted at the decision level for further boosting the performance. We perform an extensive evaluation on the challenge data (including validation set and blind test set), and evaluate the effects of different strategies in our pipeline. The final recognition accuracy achieved 50.4% on test set, with a significant gain of 16.7% above the challenge baseline 33.7%. 2014-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6387 info:doi/10.1145/2663204.2666274 https://ink.library.smu.edu.sg/context/sis_research/article/7390/viewcontent/Combining_multiple_kernel_methods_on_riemannian_manifold_for_emotion_recognition_in_the_wild.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Emotion recognition; EmotiW 2014 challenge; Multiple kernels; Riemannian manifold Databases and Information Systems Graphics and Human Computer Interfaces |
institution |
Singapore Management University |
building |
SMU Libraries |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
SMU Libraries |
collection |
InK@SMU |
language |
English |
topic |
Emotion recognition; EmotiW 2014 challenge; Multiple kernels; Riemannian manifold Databases and Information Systems Graphics and Human Computer Interfaces |
spellingShingle |
Emotion recognition; EmotiW 2014 challenge; Multiple kernels; Riemannian manifold Databases and Information Systems Graphics and Human Computer Interfaces LIU, M. WANG, R. LI, S. SHAN, S. HUANG, Zhiwu CHEN, X. Combining multiple kernel methods on Riemannian manifold for emotion recognition in the wild |
description |
In this paper, we present the method for our submission to the Emotion Recognition in the Wild Challenge (EmotiW 2014). The challenge is to automatically classify the emotions acted by human subjects in video clips under realworld environment. In our method, each video clip can be represented by three types of image set models (i.e. linear subspace, covariance matrix, and Gaussian distribution) respectively, which can all be viewed as points residing on some Riemannian manifolds. Then different Riemannian kernels are employed on these set models correspondingly for similarity/distance measurement. For classification, three types of classifiers, i.e. kernel SVM, logistic regression, and partial least squares, are investigated for comparisons. Finally, an optimal fusion of classifiers learned from different kernels and different modalities (video and audio) is conducted at the decision level for further boosting the performance. We perform an extensive evaluation on the challenge data (including validation set and blind test set), and evaluate the effects of different strategies in our pipeline. The final recognition accuracy achieved 50.4% on test set, with a significant gain of 16.7% above the challenge baseline 33.7%. |
format |
text |
author |
LIU, M. WANG, R. LI, S. SHAN, S. HUANG, Zhiwu CHEN, X. |
author_facet |
LIU, M. WANG, R. LI, S. SHAN, S. HUANG, Zhiwu CHEN, X. |
author_sort |
LIU, M. |
title |
Combining multiple kernel methods on Riemannian manifold for emotion recognition in the wild |
title_short |
Combining multiple kernel methods on Riemannian manifold for emotion recognition in the wild |
title_full |
Combining multiple kernel methods on Riemannian manifold for emotion recognition in the wild |
title_fullStr |
Combining multiple kernel methods on Riemannian manifold for emotion recognition in the wild |
title_full_unstemmed |
Combining multiple kernel methods on Riemannian manifold for emotion recognition in the wild |
title_sort |
combining multiple kernel methods on riemannian manifold for emotion recognition in the wild |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2014 |
url |
https://ink.library.smu.edu.sg/sis_research/6387 https://ink.library.smu.edu.sg/context/sis_research/article/7390/viewcontent/Combining_multiple_kernel_methods_on_riemannian_manifold_for_emotion_recognition_in_the_wild.pdf |
_version_ |
1770575950936801280 |