Facial Expression Emotion Detection for Real-Time Embedded Systems
Recently, real-time facial expression recognition has attracted more and more research. In this study, an automatic facial expression real-time system was built and tested. Firstly, the system and model were designed and tested on a MATLAB environment followed by a MATLAB Simulink environment that i...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-01-01
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| Series: | Technologies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2227-7080/6/1/17 |
| _version_ | 1811270045050863616 |
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| author | Saeed Turabzadeh Hongying Meng Rafiq M. Swash Matus Pleva Jozef Juhar |
| author_facet | Saeed Turabzadeh Hongying Meng Rafiq M. Swash Matus Pleva Jozef Juhar |
| author_sort | Saeed Turabzadeh |
| collection | DOAJ |
| description | Recently, real-time facial expression recognition has attracted more and more research. In this study, an automatic facial expression real-time system was built and tested. Firstly, the system and model were designed and tested on a MATLAB environment followed by a MATLAB Simulink environment that is capable of recognizing continuous facial expressions in real-time with a rate of 1 frame per second and that is implemented on a desktop PC. They have been evaluated in a public dataset, and the experimental results were promising. The dataset and labels used in this study were made from videos, which were recorded twice from five participants while watching a video. Secondly, in order to implement in real-time at a faster frame rate, the facial expression recognition system was built on the field-programmable gate array (FPGA). The camera sensor used in this work was a Digilent VmodCAM — stereo camera module. The model was built on the Atlys™ Spartan-6 FPGA development board. It can continuously perform emotional state recognition in real-time at a frame rate of 30. A graphical user interface was designed to display the participant’s video in real-time and two-dimensional predict labels of the emotion at the same time. |
| first_indexed | 2024-04-12T21:53:51Z |
| format | Article |
| id | doaj.art-4a7882daaa9d4e2486c0a1cf1a9fcc04 |
| institution | Directory Open Access Journal |
| issn | 2227-7080 |
| language | English |
| last_indexed | 2024-04-12T21:53:51Z |
| publishDate | 2018-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj.art-4a7882daaa9d4e2486c0a1cf1a9fcc042022-12-22T03:15:23ZengMDPI AGTechnologies2227-70802018-01-01611710.3390/technologies6010017technologies6010017Facial Expression Emotion Detection for Real-Time Embedded SystemsSaeed Turabzadeh0Hongying Meng1Rafiq M. Swash2Matus Pleva3Jozef Juhar4Department of Electronic and Computer Engineering, Brunel University London, Uxbridge UB8 3PH, UKDepartment of Electronic and Computer Engineering, Brunel University London, Uxbridge UB8 3PH, UKDepartment of Electronic and Computer Engineering, Brunel University London, Uxbridge UB8 3PH, UKDepartment of Electronics and Multimedia Telecommunications, Technical University of Kosice, Letna 9, 04001 Kosice, SlovakiaDepartment of Electronics and Multimedia Telecommunications, Technical University of Kosice, Letna 9, 04001 Kosice, SlovakiaRecently, real-time facial expression recognition has attracted more and more research. In this study, an automatic facial expression real-time system was built and tested. Firstly, the system and model were designed and tested on a MATLAB environment followed by a MATLAB Simulink environment that is capable of recognizing continuous facial expressions in real-time with a rate of 1 frame per second and that is implemented on a desktop PC. They have been evaluated in a public dataset, and the experimental results were promising. The dataset and labels used in this study were made from videos, which were recorded twice from five participants while watching a video. Secondly, in order to implement in real-time at a faster frame rate, the facial expression recognition system was built on the field-programmable gate array (FPGA). The camera sensor used in this work was a Digilent VmodCAM — stereo camera module. The model was built on the Atlys™ Spartan-6 FPGA development board. It can continuously perform emotional state recognition in real-time at a frame rate of 30. A graphical user interface was designed to display the participant’s video in real-time and two-dimensional predict labels of the emotion at the same time.http://www.mdpi.com/2227-7080/6/1/17FPGAfacial expression analysisartificial intelligencereal-time implementation |
| spellingShingle | Saeed Turabzadeh Hongying Meng Rafiq M. Swash Matus Pleva Jozef Juhar Facial Expression Emotion Detection for Real-Time Embedded Systems Technologies FPGA facial expression analysis artificial intelligence real-time implementation |
| title | Facial Expression Emotion Detection for Real-Time Embedded Systems |
| title_full | Facial Expression Emotion Detection for Real-Time Embedded Systems |
| title_fullStr | Facial Expression Emotion Detection for Real-Time Embedded Systems |
| title_full_unstemmed | Facial Expression Emotion Detection for Real-Time Embedded Systems |
| title_short | Facial Expression Emotion Detection for Real-Time Embedded Systems |
| title_sort | facial expression emotion detection for real time embedded systems |
| topic | FPGA facial expression analysis artificial intelligence real-time implementation |
| url | http://www.mdpi.com/2227-7080/6/1/17 |
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