A novel framework using binary attention mechanism based deep convolution neural network for face emotion recognition
The use of facial emotion recognition (FER) technologies will become more pervasive in our everyday lives. Emotional awareness is advantageous for many types of businesses and areas of life. It is advantageous and important for reasons of security and heath. Deep hierarchical FER systems often focus...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917423002179 |
| _version_ | 1797454266376388608 |
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| author | Radha Priyadharsini G Krishnaveni K |
| author_facet | Radha Priyadharsini G Krishnaveni K |
| author_sort | Radha Priyadharsini G |
| collection | DOAJ |
| description | The use of facial emotion recognition (FER) technologies will become more pervasive in our everyday lives. Emotional awareness is advantageous for many types of businesses and areas of life. It is advantageous and important for reasons of security and heath. Deep hierarchical FER systems often focus on the following two main problems; going out of control due to identification factors including lighting, face location, and recognition bias, as well as a lack of training data. We developed each Deep Convolutional Neural Network (DCNN) based on a Binary Attention Mechanism (BAM) for the facial emotion recognition issue in our proposed system. Each image of a face has to be assigned to one of the seven facial emotions. An updated BAM-DCNN model was trained using the original pixel data characteristics. The Histogram of Oriented Gradients (HOG) is used for data preparation. To lessen the overfitting of the models, we used dropout and batch normalization in addition to L2 regularization. The recommended technique enables the detection of human emotion in images by automatically recognizing, extracting, and evaluating diverse face expressions. We extract and examine performance assessment measures from FER datasets, including recognition accuracy, precision, sensitivity, specificity, recall, and F1 score. To demonstrate the effectiveness of our system, we also contrast the recommended technique with the practices now in use. |
| first_indexed | 2024-03-09T15:35:47Z |
| format | Article |
| id | doaj.art-2b9ee6f9eb774cd8821a8d732505a9d3 |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-03-09T15:35:47Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-2b9ee6f9eb774cd8821a8d732505a9d32023-11-26T05:13:44ZengElsevierMeasurement: Sensors2665-91742023-12-0130100881A novel framework using binary attention mechanism based deep convolution neural network for face emotion recognitionRadha Priyadharsini G0Krishnaveni K1Corresponding author.; Department of Computer Science, Sri S.Ramasamy Naidu Memorial College, Sattur, Tamilnadu, IndiaDepartment of Computer Science, Sri S.Ramasamy Naidu Memorial College, Sattur, Tamilnadu, IndiaThe use of facial emotion recognition (FER) technologies will become more pervasive in our everyday lives. Emotional awareness is advantageous for many types of businesses and areas of life. It is advantageous and important for reasons of security and heath. Deep hierarchical FER systems often focus on the following two main problems; going out of control due to identification factors including lighting, face location, and recognition bias, as well as a lack of training data. We developed each Deep Convolutional Neural Network (DCNN) based on a Binary Attention Mechanism (BAM) for the facial emotion recognition issue in our proposed system. Each image of a face has to be assigned to one of the seven facial emotions. An updated BAM-DCNN model was trained using the original pixel data characteristics. The Histogram of Oriented Gradients (HOG) is used for data preparation. To lessen the overfitting of the models, we used dropout and batch normalization in addition to L2 regularization. The recommended technique enables the detection of human emotion in images by automatically recognizing, extracting, and evaluating diverse face expressions. We extract and examine performance assessment measures from FER datasets, including recognition accuracy, precision, sensitivity, specificity, recall, and F1 score. To demonstrate the effectiveness of our system, we also contrast the recommended technique with the practices now in use.http://www.sciencedirect.com/science/article/pii/S2665917423002179Face emotion recognition (FER)Binary attention mechanism-deep convolutional neural network (BAM-DCNN)Binary attention mechanism (BAM)Histogram of oriented gradients (HOG) |
| spellingShingle | Radha Priyadharsini G Krishnaveni K A novel framework using binary attention mechanism based deep convolution neural network for face emotion recognition Measurement: Sensors Face emotion recognition (FER) Binary attention mechanism-deep convolutional neural network (BAM-DCNN) Binary attention mechanism (BAM) Histogram of oriented gradients (HOG) |
| title | A novel framework using binary attention mechanism based deep convolution neural network for face emotion recognition |
| title_full | A novel framework using binary attention mechanism based deep convolution neural network for face emotion recognition |
| title_fullStr | A novel framework using binary attention mechanism based deep convolution neural network for face emotion recognition |
| title_full_unstemmed | A novel framework using binary attention mechanism based deep convolution neural network for face emotion recognition |
| title_short | A novel framework using binary attention mechanism based deep convolution neural network for face emotion recognition |
| title_sort | novel framework using binary attention mechanism based deep convolution neural network for face emotion recognition |
| topic | Face emotion recognition (FER) Binary attention mechanism-deep convolutional neural network (BAM-DCNN) Binary attention mechanism (BAM) Histogram of oriented gradients (HOG) |
| url | http://www.sciencedirect.com/science/article/pii/S2665917423002179 |
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