Face emotion recognition based on infrared thermal imagery by applying machine learning and parallelism
Over time for the past few years, facial expression identification has been a promising area. However, darkness, lighting conditions, and other factors make facial emotion identification challenging to detect. As a result, thermal images are suggested as a solution to such problems and for a variety...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2023-01-01
|
| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023042?viewType=HTML |
| _version_ | 1797985216910852096 |
|---|---|
| author | Basem Assiri Mohammad Alamgir Hossain |
| author_facet | Basem Assiri Mohammad Alamgir Hossain |
| author_sort | Basem Assiri |
| collection | DOAJ |
| description | Over time for the past few years, facial expression identification has been a promising area. However, darkness, lighting conditions, and other factors make facial emotion identification challenging to detect. As a result, thermal images are suggested as a solution to such problems and for a variety of other benefits. Furthermore, focusing on significant regions of a face rather than the entire face is sufficient for reducing processing and improving accuracy at the same time. This research introduces novel infrared thermal image-based approaches for facial emotion recognition. First, the entire image of the face is separated into four pieces. Then, we accepted only four active regions (ARs) to prepare training and testing datasets. These four ARs are the left eye, right eye, and lips areas. In addition, ten-folded cross-validation is proposed to improve recognition accuracy using Convolutional Neural Network (CNN), a machine learning technique. Furthermore, we incorporated a parallelism technique to reduce processing-time in testing and training datasets. As a result, we have seen that the processing time reduces to 50%. Finally, a decision-level fusion is applied to improve the recognition accuracy. As a result, the proposed technique achieves a recognition accuracy of 96.87 %. The achieved accuracy ascertains the robustness of our proposed scheme. |
| first_indexed | 2024-04-11T07:13:47Z |
| format | Article |
| id | doaj.art-c312c16f82c1401d92b67fefe67a7b63 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-04-11T07:13:47Z |
| publishDate | 2023-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-c312c16f82c1401d92b67fefe67a7b632022-12-22T04:38:04ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-01-0120191392910.3934/mbe.2023042Face emotion recognition based on infrared thermal imagery by applying machine learning and parallelismBasem Assiri 0Mohammad Alamgir Hossain 1Department of Computer Science, College of CS & IT, Jazan University, Kingdom of Saudi ArabiaDepartment of Computer Science, College of CS & IT, Jazan University, Kingdom of Saudi ArabiaOver time for the past few years, facial expression identification has been a promising area. However, darkness, lighting conditions, and other factors make facial emotion identification challenging to detect. As a result, thermal images are suggested as a solution to such problems and for a variety of other benefits. Furthermore, focusing on significant regions of a face rather than the entire face is sufficient for reducing processing and improving accuracy at the same time. This research introduces novel infrared thermal image-based approaches for facial emotion recognition. First, the entire image of the face is separated into four pieces. Then, we accepted only four active regions (ARs) to prepare training and testing datasets. These four ARs are the left eye, right eye, and lips areas. In addition, ten-folded cross-validation is proposed to improve recognition accuracy using Convolutional Neural Network (CNN), a machine learning technique. Furthermore, we incorporated a parallelism technique to reduce processing-time in testing and training datasets. As a result, we have seen that the processing time reduces to 50%. Finally, a decision-level fusion is applied to improve the recognition accuracy. As a result, the proposed technique achieves a recognition accuracy of 96.87 %. The achieved accuracy ascertains the robustness of our proposed scheme.https://www.aimspress.com/article/doi/10.3934/mbe.2023042?viewType=HTMLfacial emotion recognitioninfrared imageactive regionmachine learningparallelism |
| spellingShingle | Basem Assiri Mohammad Alamgir Hossain Face emotion recognition based on infrared thermal imagery by applying machine learning and parallelism Mathematical Biosciences and Engineering facial emotion recognition infrared image active region machine learning parallelism |
| title | Face emotion recognition based on infrared thermal imagery by applying machine learning and parallelism |
| title_full | Face emotion recognition based on infrared thermal imagery by applying machine learning and parallelism |
| title_fullStr | Face emotion recognition based on infrared thermal imagery by applying machine learning and parallelism |
| title_full_unstemmed | Face emotion recognition based on infrared thermal imagery by applying machine learning and parallelism |
| title_short | Face emotion recognition based on infrared thermal imagery by applying machine learning and parallelism |
| title_sort | face emotion recognition based on infrared thermal imagery by applying machine learning and parallelism |
| topic | facial emotion recognition infrared image active region machine learning parallelism |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023042?viewType=HTML |
| work_keys_str_mv | AT basemassiri faceemotionrecognitionbasedoninfraredthermalimagerybyapplyingmachinelearningandparallelism AT mohammadalamgirhossain faceemotionrecognitionbasedoninfraredthermalimagerybyapplyingmachinelearningandparallelism |