An improved approach for depth data based face pose estimation using particle swarm optimization
This paper presents an improved approach for face pose estimation based on depth data using particle swarm optimization (PSO). In this approach, the frontal face of the system-user is first initialized and its depth image is taken as a person-specific template. Each query face of that user is rotate...
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2015
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| Online Access: | https://hdl.handle.net/10356/106043 http://hdl.handle.net/10220/26272 http://dx.doi.org/10.5220/0004732305340541 |
| _version_ | 1811688226676539392 |
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| author | Wang, Han Mou, Xiaozheng |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Wang, Han Mou, Xiaozheng |
| author_sort | Wang, Han |
| collection | NTU |
| description | This paper presents an improved approach for face pose estimation based on depth data using particle swarm optimization (PSO). In this approach, the frontal face of the system-user is first initialized and its depth image is taken as a person-specific template. Each query face of that user is rotated and translated with respect to its centroid using PSO to match with the template. Since the centroid of each query face always changes with the face pose changing, a common reference point has to be defined to measure the exact transformation of the query face. Thus, the nose tips of the optimal transformed face and the query face are localized to recomputed the transformation from the query face to the optimal transformed face that matched with the template. Using the recomputed rotation and translation information, finally, the pose of the query face can be approximated by the relative pose between the query face and the template face. Experiments on public database show that the accuracy of this new method is more than 99%, which is much higher than the best performance (< 91%) of existing work. |
| first_indexed | 2024-10-01T05:28:51Z |
| format | Conference Paper |
| id | ntu-10356/106043 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:28:51Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | ntu-10356/1060432019-12-06T22:03:32Z An improved approach for depth data based face pose estimation using particle swarm optimization Wang, Han Mou, Xiaozheng School of Electrical and Electronic Engineering The 9th International Conference on Computer Vision Theory and Applications DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems This paper presents an improved approach for face pose estimation based on depth data using particle swarm optimization (PSO). In this approach, the frontal face of the system-user is first initialized and its depth image is taken as a person-specific template. Each query face of that user is rotated and translated with respect to its centroid using PSO to match with the template. Since the centroid of each query face always changes with the face pose changing, a common reference point has to be defined to measure the exact transformation of the query face. Thus, the nose tips of the optimal transformed face and the query face are localized to recomputed the transformation from the query face to the optimal transformed face that matched with the template. Using the recomputed rotation and translation information, finally, the pose of the query face can be approximated by the relative pose between the query face and the template face. Experiments on public database show that the accuracy of this new method is more than 99%, which is much higher than the best performance (< 91%) of existing work. Published version 2015-07-06T02:24:38Z 2019-12-06T22:03:31Z 2015-07-06T02:24:38Z 2019-12-06T22:03:31Z 2014 2014 Conference Paper Mou, X., & Wang, H. (2014). An improved approach for depth data based face pose estimation using particle swarm optimization. Proceedings of the 9th International Conference on Computer Vision Theory and Applications, 534-541. https://hdl.handle.net/10356/106043 http://hdl.handle.net/10220/26272 http://dx.doi.org/10.5220/0004732305340541 en © 2014 SCITEPRESS - Science and Technology Publications. This paper was published in Proceedings of the 9th International Conference on Computer Vision Theory and Applications and is made available as an electronic reprint (preprint) with permission of SCITEPRESS - Science and Technology Publications. The published version is available at: [http://dx.doi.org/10.5220/0004732305340541]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Wang, Han Mou, Xiaozheng An improved approach for depth data based face pose estimation using particle swarm optimization |
| title | An improved approach for depth data based face pose estimation using particle swarm optimization |
| title_full | An improved approach for depth data based face pose estimation using particle swarm optimization |
| title_fullStr | An improved approach for depth data based face pose estimation using particle swarm optimization |
| title_full_unstemmed | An improved approach for depth data based face pose estimation using particle swarm optimization |
| title_short | An improved approach for depth data based face pose estimation using particle swarm optimization |
| title_sort | improved approach for depth data based face pose estimation using particle swarm optimization |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems |
| url | https://hdl.handle.net/10356/106043 http://hdl.handle.net/10220/26272 http://dx.doi.org/10.5220/0004732305340541 |
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