Two new methods for facial expression recognition using Convolutional Neural Networks Article Swipe
Jinfeng Wang
,
Xuegang Wang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2031/1/012023
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2031/1/012023
In this research, we propose two novel methods for facial expression recognition to improve the accuracy of recognition. The first of our novel approach is to add the Batch Normalization (BN) layer to the CNN model, and the second of the novel approach is to preprocess the image before image training, such as rotating image, cropping the image and adding Gaussian noise in the picture, especially it is beneficial for unbalanced classifications. Our model consists of 3 CNN layers, 3 BN layers, three average-pooling layers, and three fully-connected layers; our model has a satisfying performance on the prediction category after adopting the two methods mentioned above. Our CNN model is trained and tested with Kaggle facial expression recognition challenge databases. The implemented system can automatically recognize seven expressions in real-time: anger, disgust, fear, happiness, neutral, sadness, and sur-prise. The experimental results demonstrate the effectiveness of our proposed approach.
Related Topics
Concepts
Computer science
Convolutional neural network
Artificial intelligence
Sadness
Disgust
Facial expression
Normalization (sociology)
Pattern recognition (psychology)
Pooling
Image (mathematics)
Facial expression recognition
Speech recognition
Facial recognition system
Anger
Psychology
Anthropology
Psychiatry
Sociology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/2031/1/012023
- OA Status
- diamond
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3203828829
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3203828829Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1742-6596/2031/1/012023Digital Object Identifier
- Title
-
Two new methods for facial expression recognition using Convolutional Neural NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
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2021-09-01Full publication date if available
- Authors
-
Jinfeng Wang, Xuegang WangList of authors in order
- Landing page
-
https://doi.org/10.1088/1742-6596/2031/1/012023Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1088/1742-6596/2031/1/012023Direct OA link when available
- Concepts
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Computer science, Convolutional neural network, Artificial intelligence, Sadness, Disgust, Facial expression, Normalization (sociology), Pattern recognition (psychology), Pooling, Image (mathematics), Facial expression recognition, Speech recognition, Facial recognition system, Anger, Psychology, Anthropology, Psychiatry, SociologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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19Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.improve | 14 |
| abstract_inverted_index.layers, | 79, 82, 85 |
| abstract_inverted_index.layers; | 89 |
| abstract_inverted_index.methods | 8, 104 |
| abstract_inverted_index.propose | 5 |
| abstract_inverted_index.results | 141 |
| abstract_inverted_index.trained | 111 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Gaussian | 61 |
| abstract_inverted_index.accuracy | 16 |
| abstract_inverted_index.adopting | 101 |
| abstract_inverted_index.approach | 24, 43 |
| abstract_inverted_index.category | 99 |
| abstract_inverted_index.consists | 75 |
| abstract_inverted_index.cropping | 56 |
| abstract_inverted_index.disgust, | 132 |
| abstract_inverted_index.neutral, | 135 |
| abstract_inverted_index.picture, | 65 |
| abstract_inverted_index.proposed | 147 |
| abstract_inverted_index.rotating | 54 |
| abstract_inverted_index.sadness, | 136 |
| abstract_inverted_index.approach. | 148 |
| abstract_inverted_index.challenge | 119 |
| abstract_inverted_index.mentioned | 105 |
| abstract_inverted_index.recognize | 126 |
| abstract_inverted_index.research, | 3 |
| abstract_inverted_index.training, | 51 |
| abstract_inverted_index.beneficial | 69 |
| abstract_inverted_index.databases. | 120 |
| abstract_inverted_index.especially | 66 |
| abstract_inverted_index.expression | 11, 117 |
| abstract_inverted_index.happiness, | 134 |
| abstract_inverted_index.prediction | 98 |
| abstract_inverted_index.preprocess | 46 |
| abstract_inverted_index.real-time: | 130 |
| abstract_inverted_index.satisfying | 94 |
| abstract_inverted_index.sur-prise. | 138 |
| abstract_inverted_index.unbalanced | 71 |
| abstract_inverted_index.demonstrate | 142 |
| abstract_inverted_index.expressions | 128 |
| abstract_inverted_index.implemented | 122 |
| abstract_inverted_index.performance | 95 |
| abstract_inverted_index.recognition | 12, 118 |
| abstract_inverted_index.experimental | 140 |
| abstract_inverted_index.recognition. | 18 |
| abstract_inverted_index.Normalization | 30 |
| abstract_inverted_index.automatically | 125 |
| abstract_inverted_index.effectiveness | 144 |
| abstract_inverted_index.average-pooling | 84 |
| abstract_inverted_index.fully-connected | 88 |
| abstract_inverted_index.classifications. | 72 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5050556614, https://openalex.org/A5100344683 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I113281461, https://openalex.org/I4210098369 |
| citation_normalized_percentile.value | 0.12360285 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |