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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Gee-Sern Hsu Yu-Lun Liu Hsiao-Chia Peng Sheng-Luen Chung |
| Copyright Year | 2014 |
| Description | Author affiliation: Taipei, Nat. Taiwan Univ. of Sci. & Technol., Taipei, Taiwan (Gee-Sern Hsu; Yu-Lun Liu; Hsiao-Chia Peng; Sheng-Luen Chung) |
| Abstract | Most RGB-D based research focuses on gesture analysis, scene reconstruction and SLAM, but only few study its impacts on face recognition. A common yet challenging scenario considered in face recognition across pose takes a single 2D face of frontal pose as the galley and other poses as the probe set. We consider a similar scenario but with a RGB-D image pair taken at frontal pose in the gallery, only 2D images with a large scope of poses in the probe set, and study the advantage of the additional depth map on top of the regular RGB image. We formulate the 3D face reconstruction using the RGB-D image as a constrained optimization, and compare the results with different reconstruction settings. The reconstructed 3D face allows the generation of 2D face with specific poses, which can be matched with the probes. Experiments on the Biwi Kinect Head Pose Database and Eurecom Database show that the additional depth map substantially improves the cross-pose recognition performance, and the depth-based component selection also improves the recognition under occlusion and expression variation. |
| Starting Page | 339 |
| Ending Page | 344 |
| File Size | 999071 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479952090 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2014.67 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-24 |
| Publisher Place | Sweden |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Face Three-dimensional displays Image reconstruction Face recognition Probes Solid modeling Cameras |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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