Facial Feature Extraction Using a Symmetric Inline Matrix-LBP Variant for Emotion Recognition
With a large number of Local Binary Patterns (LBP) variants being currently used today, the significant and importance of visual descriptors in computer vision applications are prominent. This paper presents a novel visual descriptor, i.e., SIM-LBP. It employs a new matrix technique called the Symme...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/22/8635 |
| _version_ | 1797464033094270976 |
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| author | Eaby Kollonoor Babu Kamlesh Mistry Muhammad Naveed Anwar Li Zhang |
| author_facet | Eaby Kollonoor Babu Kamlesh Mistry Muhammad Naveed Anwar Li Zhang |
| author_sort | Eaby Kollonoor Babu |
| collection | DOAJ |
| description | With a large number of Local Binary Patterns (LBP) variants being currently used today, the significant and importance of visual descriptors in computer vision applications are prominent. This paper presents a novel visual descriptor, i.e., SIM-LBP. It employs a new matrix technique called the Symmetric Inline Matrix generator method, which acts as a new variant of LBP. The key feature that separates our variant from existing counterparts is that our variant is very efficient in extracting facial expression features like eyes, eye brows, nose and mouth in a wide range of lighting conditions. For testing our model, we applied SIM-LBP on the JAFFE dataset to convert all the images to its corresponding SIM-LBP transformed variant. These transformed images are then used to train a Convolution Neural Network (CNN) based deep learning model for facial expressions recognition (FER). Several performance evaluation metrics, i.e., recognition accuracy rate, precision, recall, and F1-score, were used to test mode efficiency in comparison with those using the traditional LBP descriptor and other LBP variants. Our model outperformed in all four matrices with the proposed SIM-LBP transformation on the input images against those of baseline methods. In comparison analysis with the other state-of-the-art methods, it shows the usefulness of the proposed SIM-LBP model. Our proposed SIM-LBP variant transformation can also be applied on facial images to identify a person’s mental states and predict mood variations. |
| first_indexed | 2024-03-09T18:01:11Z |
| format | Article |
| id | doaj.art-9b1302bd1a4c476c9b93e2860791c1cf |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T18:01:11Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9b1302bd1a4c476c9b93e2860791c1cf2023-11-24T09:53:21ZengMDPI AGSensors1424-82202022-11-012222863510.3390/s22228635Facial Feature Extraction Using a Symmetric Inline Matrix-LBP Variant for Emotion RecognitionEaby Kollonoor Babu0Kamlesh Mistry1Muhammad Naveed Anwar2Li Zhang3Faculty of Engineering and Environment, Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UKFaculty of Engineering and Environment, Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UKFaculty of Engineering and Environment, Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UKDepartment of Computer Science, Royal Holloway, University of London, Surrey TW20 0EX, UKWith a large number of Local Binary Patterns (LBP) variants being currently used today, the significant and importance of visual descriptors in computer vision applications are prominent. This paper presents a novel visual descriptor, i.e., SIM-LBP. It employs a new matrix technique called the Symmetric Inline Matrix generator method, which acts as a new variant of LBP. The key feature that separates our variant from existing counterparts is that our variant is very efficient in extracting facial expression features like eyes, eye brows, nose and mouth in a wide range of lighting conditions. For testing our model, we applied SIM-LBP on the JAFFE dataset to convert all the images to its corresponding SIM-LBP transformed variant. These transformed images are then used to train a Convolution Neural Network (CNN) based deep learning model for facial expressions recognition (FER). Several performance evaluation metrics, i.e., recognition accuracy rate, precision, recall, and F1-score, were used to test mode efficiency in comparison with those using the traditional LBP descriptor and other LBP variants. Our model outperformed in all four matrices with the proposed SIM-LBP transformation on the input images against those of baseline methods. In comparison analysis with the other state-of-the-art methods, it shows the usefulness of the proposed SIM-LBP model. Our proposed SIM-LBP variant transformation can also be applied on facial images to identify a person’s mental states and predict mood variations.https://www.mdpi.com/1424-8220/22/22/8635local binary patternsadaptive image transformationcoded visual descriptorsimage encodingfacial expression recognition |
| spellingShingle | Eaby Kollonoor Babu Kamlesh Mistry Muhammad Naveed Anwar Li Zhang Facial Feature Extraction Using a Symmetric Inline Matrix-LBP Variant for Emotion Recognition Sensors local binary patterns adaptive image transformation coded visual descriptors image encoding facial expression recognition |
| title | Facial Feature Extraction Using a Symmetric Inline Matrix-LBP Variant for Emotion Recognition |
| title_full | Facial Feature Extraction Using a Symmetric Inline Matrix-LBP Variant for Emotion Recognition |
| title_fullStr | Facial Feature Extraction Using a Symmetric Inline Matrix-LBP Variant for Emotion Recognition |
| title_full_unstemmed | Facial Feature Extraction Using a Symmetric Inline Matrix-LBP Variant for Emotion Recognition |
| title_short | Facial Feature Extraction Using a Symmetric Inline Matrix-LBP Variant for Emotion Recognition |
| title_sort | facial feature extraction using a symmetric inline matrix lbp variant for emotion recognition |
| topic | local binary patterns adaptive image transformation coded visual descriptors image encoding facial expression recognition |
| url | https://www.mdpi.com/1424-8220/22/22/8635 |
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