Robust representation and recognition of facial emotions using extreme sparse learning
Recognition of natural emotions from human faces is an interesting topic with a wide range of potential applications, such as human-computer interaction, automated tutoring systems, image and video retrieval, smart environments, and driver warning systems. Traditionally, facial emotion recognition s...
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| Format: | Journal Article |
| Sprache: | English |
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2015
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| Online Zugang: | https://hdl.handle.net/10356/103128 http://hdl.handle.net/10220/25737 |
| _version_ | 1826119405045547008 |
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| author | Li, Jun Teoh, Eam Khwang Nandakumar, Karthik Shojaeilangari, Seyedehsamaneh Yau, Wei-Yun |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Li, Jun Teoh, Eam Khwang Nandakumar, Karthik Shojaeilangari, Seyedehsamaneh Yau, Wei-Yun |
| author_sort | Li, Jun |
| collection | NTU |
| description | Recognition of natural emotions from human faces is an interesting topic with a wide range of potential applications, such as human-computer interaction, automated tutoring systems, image and video retrieval, smart environments, and driver warning systems. Traditionally, facial emotion recognition systems have been evaluated on laboratory controlled data, which is not representative of the environment faced in real-world applications. To robustly recognize the facial emotions in real-world natural situations, this paper proposes an approach called extreme sparse learning, which has the ability to jointly learn a dictionary (set of basis) and a nonlinear classification model. The proposed approach combines the discriminative power of extreme learning machine with the reconstruction property of sparse representation to enable accurate classification when presented with noisy signals and imperfect data recorded in natural settings. In addition, this paper presents a new local spatio-temporal descriptor that is distinctive and pose-invariant. The proposed framework is able to achieve the state-of-the-art recognition accuracy on both acted and spontaneous facial emotion databases. |
| first_indexed | 2024-10-01T04:59:25Z |
| format | Journal Article |
| id | ntu-10356/103128 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:59:25Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | ntu-10356/1031282020-03-07T14:00:33Z Robust representation and recognition of facial emotions using extreme sparse learning Li, Jun Teoh, Eam Khwang Nandakumar, Karthik Shojaeilangari, Seyedehsamaneh Yau, Wei-Yun School of Electrical and Electronic Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Recognition of natural emotions from human faces is an interesting topic with a wide range of potential applications, such as human-computer interaction, automated tutoring systems, image and video retrieval, smart environments, and driver warning systems. Traditionally, facial emotion recognition systems have been evaluated on laboratory controlled data, which is not representative of the environment faced in real-world applications. To robustly recognize the facial emotions in real-world natural situations, this paper proposes an approach called extreme sparse learning, which has the ability to jointly learn a dictionary (set of basis) and a nonlinear classification model. The proposed approach combines the discriminative power of extreme learning machine with the reconstruction property of sparse representation to enable accurate classification when presented with noisy signals and imperfect data recorded in natural settings. In addition, this paper presents a new local spatio-temporal descriptor that is distinctive and pose-invariant. The proposed framework is able to achieve the state-of-the-art recognition accuracy on both acted and spontaneous facial emotion databases. ASTAR (Agency for Sci., Tech. and Research, S’pore) Accepted version 2015-06-03T05:13:21Z 2019-12-06T21:06:14Z 2015-06-03T05:13:21Z 2019-12-06T21:06:14Z 2015 2015 Journal Article Shojaeilangari, S., Yau, W.-Y., Nandakumar, K., Li, J., & Teoh, E. K. (2015). Robust representation and recognition of facial emotions using extreme sparse learning. IEEE transactions on image processing, 24(7), 2140-2152. https://hdl.handle.net/10356/103128 http://hdl.handle.net/10220/25737 10.1109/TIP.2015.2416634 en IEEE transactions on image processing © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TIP.2015.2416634]. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Li, Jun Teoh, Eam Khwang Nandakumar, Karthik Shojaeilangari, Seyedehsamaneh Yau, Wei-Yun Robust representation and recognition of facial emotions using extreme sparse learning |
| title | Robust representation and recognition of facial emotions using extreme sparse learning |
| title_full | Robust representation and recognition of facial emotions using extreme sparse learning |
| title_fullStr | Robust representation and recognition of facial emotions using extreme sparse learning |
| title_full_unstemmed | Robust representation and recognition of facial emotions using extreme sparse learning |
| title_short | Robust representation and recognition of facial emotions using extreme sparse learning |
| title_sort | robust representation and recognition of facial emotions using extreme sparse learning |
| topic | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision |
| url | https://hdl.handle.net/10356/103128 http://hdl.handle.net/10220/25737 |
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