Development of a robust multi-scale featured local binary pattern for improved facial expression recognition
Compelling facial expression recognition (FER) processes have been utilized in very successful fields like computer vision, robotics, artificial intelligence, and dynamic texture recognition. However, the FER’s critical problem with traditional local binary pattern (LBP) is the loss of neighboring pix...
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| Format: | Article |
| Language: | English |
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MDPI
2020
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| Online Access: | http://eprints.sunway.edu.my/1658/ http://eprints.sunway.edu.my/1658/1/Mayeen%20Development%20of%20a%20robust.pdf |
| _version_ | 1848802107827683328 |
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| author | Suraiya, Y. Pathan, R. K. Biswas, M. Khandaker, Mayeen Uddin * Faruque, M. R. I. |
| author_facet | Suraiya, Y. Pathan, R. K. Biswas, M. Khandaker, Mayeen Uddin * Faruque, M. R. I. |
| author_sort | Suraiya, Y. |
| building | SU Institutional Repository |
| collection | Online Access |
| description | Compelling facial expression recognition (FER) processes have been utilized in very successful fields like computer vision, robotics, artificial intelligence, and dynamic texture recognition. However, the FER’s critical problem with traditional local binary pattern (LBP) is the loss of neighboring pixels related to different scales that can affect the texture of facial images. To overcome such limitations, this study describes a new extended LBP method to extract feature vectors from images, detecting each image from facial expressions. The proposed method is based on the bitwise AND operation of two rotational kernels applied on LBP(8,1)and LBP(8,2)and utilizes two accessible datasets. Firstly, the facial parts are detected and the essential components of a face are observed, such as eyes, nose, and lips. The portion of the face is then cropped to reduce the dimensions and an unsharp masking kernel is applied to sharpen the image. The filtered images then go through the feature extraction method and wait for the classification process. Four machine learning classifiers were used to verify the proposed method. This study shows that the proposed multi-scale featured local binary pattern (MSFLBP), together with Support Vector Machine (SVM), outperformed the recent LBP-based state-of-the-art approaches resulting in an accuracy of 99.12% for the Extended Cohn–Kanade(CK+) dataset and 89.08% for the Karolinska Directed Emotional Faces(KDEF)dataset. |
| first_indexed | 2025-11-14T21:18:06Z |
| format | Article |
| id | sunway-1658 |
| institution | Sunway University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T21:18:06Z |
| publishDate | 2020 |
| publisher | MDPI |
| recordtype | eprints |
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| spelling | sunway-16582021-03-24T08:37:41Z http://eprints.sunway.edu.my/1658/ Development of a robust multi-scale featured local binary pattern for improved facial expression recognition Suraiya, Y. Pathan, R. K. Biswas, M. Khandaker, Mayeen Uddin * Faruque, M. R. I. R895-920 Medical Physics/Medical Radiology Compelling facial expression recognition (FER) processes have been utilized in very successful fields like computer vision, robotics, artificial intelligence, and dynamic texture recognition. However, the FER’s critical problem with traditional local binary pattern (LBP) is the loss of neighboring pixels related to different scales that can affect the texture of facial images. To overcome such limitations, this study describes a new extended LBP method to extract feature vectors from images, detecting each image from facial expressions. The proposed method is based on the bitwise AND operation of two rotational kernels applied on LBP(8,1)and LBP(8,2)and utilizes two accessible datasets. Firstly, the facial parts are detected and the essential components of a face are observed, such as eyes, nose, and lips. The portion of the face is then cropped to reduce the dimensions and an unsharp masking kernel is applied to sharpen the image. The filtered images then go through the feature extraction method and wait for the classification process. Four machine learning classifiers were used to verify the proposed method. This study shows that the proposed multi-scale featured local binary pattern (MSFLBP), together with Support Vector Machine (SVM), outperformed the recent LBP-based state-of-the-art approaches resulting in an accuracy of 99.12% for the Extended Cohn–Kanade(CK+) dataset and 89.08% for the Karolinska Directed Emotional Faces(KDEF)dataset. MDPI 2020-09 Article PeerReviewed text en cc_by_nc_4 http://eprints.sunway.edu.my/1658/1/Mayeen%20Development%20of%20a%20robust.pdf Suraiya, Y. and Pathan, R. K. and Biswas, M. and Khandaker, Mayeen Uddin * and Faruque, M. R. I. (2020) Development of a robust multi-scale featured local binary pattern for improved facial expression recognition. Sensors, 20 (18). p. 5391. ISSN 1424-8220 http://doi.org/10.3390/s20185391 https://doi.org/10.3390/s20185391 |
| spellingShingle | R895-920 Medical Physics/Medical Radiology Suraiya, Y. Pathan, R. K. Biswas, M. Khandaker, Mayeen Uddin * Faruque, M. R. I. Development of a robust multi-scale featured local binary pattern for improved facial expression recognition |
| title | Development of a robust multi-scale featured local binary pattern for improved facial expression recognition |
| title_full | Development of a robust multi-scale featured local binary pattern for improved facial expression recognition |
| title_fullStr | Development of a robust multi-scale featured local binary pattern for improved facial expression recognition |
| title_full_unstemmed | Development of a robust multi-scale featured local binary pattern for improved facial expression recognition |
| title_short | Development of a robust multi-scale featured local binary pattern for improved facial expression recognition |
| title_sort | development of a robust multi-scale featured local binary pattern for improved facial expression recognition |
| topic | R895-920 Medical Physics/Medical Radiology |
| url | http://eprints.sunway.edu.my/1658/ http://eprints.sunway.edu.my/1658/ http://eprints.sunway.edu.my/1658/ http://eprints.sunway.edu.my/1658/1/Mayeen%20Development%20of%20a%20robust.pdf |