Direct Classification of Emotional Intensity
In this paper, we present a model that can directly predict emotion intensity score from video inputs, instead of deriving from action units. Using a 3d DNN incorporated with dynamic emotion information, we train a model using videos of different people smiling that outputs an intensity score from 0...
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| creator | Ouyang, Jacob Galatzer-Levy, Isaac R Koesmahargyo, Vidya Zhang, Li |
| description | In this paper, we present a model that can directly predict emotion intensity
score from video inputs, instead of deriving from action units. Using a 3d DNN
incorporated with dynamic emotion information, we train a model using videos of
different people smiling that outputs an intensity score from 0-10. Each video
is labeled framewise using a normalized action-unit based intensity score. Our
model then employs an adaptive learning technique to improve performance when
dealing with new subjects. Compared to other models, our model excels in
generalization between different people as well as provides a new framework to
directly classify emotional intensity. |
| doi_str_mv | 10.48550/arxiv.2011.07460 |
| format | Article |
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score from video inputs, instead of deriving from action units. Using a 3d DNN
incorporated with dynamic emotion information, we train a model using videos of
different people smiling that outputs an intensity score from 0-10. Each video
is labeled framewise using a normalized action-unit based intensity score. Our
model then employs an adaptive learning technique to improve performance when
dealing with new subjects. Compared to other models, our model excels in
generalization between different people as well as provides a new framework to
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incorporated with dynamic emotion information, we train a model using videos of
different people smiling that outputs an intensity score from 0-10. Each video
is labeled framewise using a normalized action-unit based intensity score. Our
model then employs an adaptive learning technique to improve performance when
dealing with new subjects. Compared to other models, our model excels in
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score from video inputs, instead of deriving from action units. Using a 3d DNN
incorporated with dynamic emotion information, we train a model using videos of
different people smiling that outputs an intensity score from 0-10. Each video
is labeled framewise using a normalized action-unit based intensity score. Our
model then employs an adaptive learning technique to improve performance when
dealing with new subjects. Compared to other models, our model excels in
generalization between different people as well as provides a new framework to
directly classify emotional intensity.</abstract><doi>10.48550/arxiv.2011.07460</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition Computer Science - Learning |
| title | Direct Classification of Emotional Intensity |
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