Kernel locality‐constrained sparse coding for head pose estimation
In many situations, it would be practical for a computer system user interface to have a model of where a person is looking and what the user is paying attention to. In this study, the authors describe a novel feature coding method for head pose estimation. The widely‐used sparse coding (SC) method...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-12-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2015.0242 |
| _version_ | 1797685016640094208 |
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| author | Hyunduk Kim Myoung‐Kyu Sohn Dong‐Ju Kim Sang‐Heon Lee |
| author_facet | Hyunduk Kim Myoung‐Kyu Sohn Dong‐Ju Kim Sang‐Heon Lee |
| author_sort | Hyunduk Kim |
| collection | DOAJ |
| description | In many situations, it would be practical for a computer system user interface to have a model of where a person is looking and what the user is paying attention to. In this study, the authors describe a novel feature coding method for head pose estimation. The widely‐used sparse coding (SC) method encodes a test sample using a sparse linear combination of training samples. However, it does not consider the underlying structure of the data in the feature space. In contrast, locality‐constrained linear coding (LLC) utilises locality constraints to project each input data into its local‐coordinate system. Based on the recent success of LLC, the authors introduce locality‐constrained sparse coding (LSC) to overcome the limitation of Sparse Coding. The authors also propose kernel locality‐constrained sparse coding, which is a non‐linear extension of LSC. By using kernel tricks, the authors implicitly map the input data into the kernel feature space associated with the kernel function. In experiments, the proposed algorithm was applied to a head pose estimation application. Experimental results demonstrated the increased effectiveness and robustness of the method. |
| first_indexed | 2024-03-12T00:39:09Z |
| format | Article |
| id | doaj.art-6fbc104930534792bd0cb5cd9424d04b |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:39:09Z |
| publishDate | 2016-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-6fbc104930534792bd0cb5cd9424d04b2023-09-15T09:05:40ZengWileyIET Computer Vision1751-96321751-96402016-12-0110882883510.1049/iet-cvi.2015.0242Kernel locality‐constrained sparse coding for head pose estimationHyunduk Kim0Myoung‐Kyu Sohn1Dong‐Ju Kim2Sang‐Heon Lee3Department of IoT and Robotics Convergence ResearchDGISTDaeguKoreaDepartment of IoT and Robotics Convergence ResearchDGISTDaeguKoreaDepartment of IoT and Robotics Convergence ResearchDGISTDaeguKoreaDepartment of IoT and Robotics Convergence ResearchDGISTDaeguKoreaIn many situations, it would be practical for a computer system user interface to have a model of where a person is looking and what the user is paying attention to. In this study, the authors describe a novel feature coding method for head pose estimation. The widely‐used sparse coding (SC) method encodes a test sample using a sparse linear combination of training samples. However, it does not consider the underlying structure of the data in the feature space. In contrast, locality‐constrained linear coding (LLC) utilises locality constraints to project each input data into its local‐coordinate system. Based on the recent success of LLC, the authors introduce locality‐constrained sparse coding (LSC) to overcome the limitation of Sparse Coding. The authors also propose kernel locality‐constrained sparse coding, which is a non‐linear extension of LSC. By using kernel tricks, the authors implicitly map the input data into the kernel feature space associated with the kernel function. In experiments, the proposed algorithm was applied to a head pose estimation application. Experimental results demonstrated the increased effectiveness and robustness of the method.https://doi.org/10.1049/iet-cvi.2015.0242kernel functionkernel feature spacekernel trickslocal-coordinate systemlocality-constrained linear codingtraining samples |
| spellingShingle | Hyunduk Kim Myoung‐Kyu Sohn Dong‐Ju Kim Sang‐Heon Lee Kernel locality‐constrained sparse coding for head pose estimation IET Computer Vision kernel function kernel feature space kernel tricks local-coordinate system locality-constrained linear coding training samples |
| title | Kernel locality‐constrained sparse coding for head pose estimation |
| title_full | Kernel locality‐constrained sparse coding for head pose estimation |
| title_fullStr | Kernel locality‐constrained sparse coding for head pose estimation |
| title_full_unstemmed | Kernel locality‐constrained sparse coding for head pose estimation |
| title_short | Kernel locality‐constrained sparse coding for head pose estimation |
| title_sort | kernel locality constrained sparse coding for head pose estimation |
| topic | kernel function kernel feature space kernel tricks local-coordinate system locality-constrained linear coding training samples |
| url | https://doi.org/10.1049/iet-cvi.2015.0242 |
| work_keys_str_mv | AT hyundukkim kernellocalityconstrainedsparsecodingforheadposeestimation AT myoungkyusohn kernellocalityconstrainedsparsecodingforheadposeestimation AT dongjukim kernellocalityconstrainedsparsecodingforheadposeestimation AT sangheonlee kernellocalityconstrainedsparsecodingforheadposeestimation |