Using Kinect for face recognition under varying poses, expressions, illumination and disguise
We present an algorithm that uses a low resolution 3D sensor for robust face recognition under challenging conditions. A preprocessing algorithm is proposed which exploits the facial symmetry at the 3D point cloud level to obtain a canonical frontal view, shape and texture, of the faces irrespective...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/6810 |
| _version_ | 1848745183896666112 |
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| author | Li, Billy Mian, A. Liu, Wan-Quan Krishna, Aneesh |
| author2 | Not known |
| author_facet | Not known Li, Billy Mian, A. Liu, Wan-Quan Krishna, Aneesh |
| author_sort | Li, Billy |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | We present an algorithm that uses a low resolution 3D sensor for robust face recognition under challenging conditions. A preprocessing algorithm is proposed which exploits the facial symmetry at the 3D point cloud level to obtain a canonical frontal view, shape and texture, of the faces irrespective of their initial pose. This algorithm also fills holes and smooths the noisy depth data produced by the low resolution sensor. The canonical depth map and texture of a query face are then sparse approximated from separate dictionaries learned from training data. The texture is transformed from the RGB to Discriminant Color Space before sparse coding and the reconstruction errors from the two sparse coding steps are added for individual identities in the dictionary. The query face is assigned the identity with the smallest reconstruction error. Experiments are performed using a publicly available database containing over 5000 facial images (RGB-D) with varying poses, expressions, illumination and disguise, acquired using the Kinect sensor. Recognition rates are 96.7% for the RGB-D data and 88.7% for the noisy depth data alone. Our results justify the feasibility of low resolution 3D sensors for robust face recognition. |
| first_indexed | 2025-11-14T06:13:19Z |
| format | Conference Paper |
| id | curtin-20.500.11937-6810 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:13:19Z |
| publishDate | 2013 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-68102018-03-29T09:05:40Z Using Kinect for face recognition under varying poses, expressions, illumination and disguise Li, Billy Mian, A. Liu, Wan-Quan Krishna, Aneesh Not known We present an algorithm that uses a low resolution 3D sensor for robust face recognition under challenging conditions. A preprocessing algorithm is proposed which exploits the facial symmetry at the 3D point cloud level to obtain a canonical frontal view, shape and texture, of the faces irrespective of their initial pose. This algorithm also fills holes and smooths the noisy depth data produced by the low resolution sensor. The canonical depth map and texture of a query face are then sparse approximated from separate dictionaries learned from training data. The texture is transformed from the RGB to Discriminant Color Space before sparse coding and the reconstruction errors from the two sparse coding steps are added for individual identities in the dictionary. The query face is assigned the identity with the smallest reconstruction error. Experiments are performed using a publicly available database containing over 5000 facial images (RGB-D) with varying poses, expressions, illumination and disguise, acquired using the Kinect sensor. Recognition rates are 96.7% for the RGB-D data and 88.7% for the noisy depth data alone. Our results justify the feasibility of low resolution 3D sensors for robust face recognition. 2013 Conference Paper http://hdl.handle.net/20.500.11937/6810 10.1109/WACV.2013.6475017 IEEE restricted |
| spellingShingle | Li, Billy Mian, A. Liu, Wan-Quan Krishna, Aneesh Using Kinect for face recognition under varying poses, expressions, illumination and disguise |
| title | Using Kinect for face recognition under varying poses, expressions, illumination and disguise |
| title_full | Using Kinect for face recognition under varying poses, expressions, illumination and disguise |
| title_fullStr | Using Kinect for face recognition under varying poses, expressions, illumination and disguise |
| title_full_unstemmed | Using Kinect for face recognition under varying poses, expressions, illumination and disguise |
| title_short | Using Kinect for face recognition under varying poses, expressions, illumination and disguise |
| title_sort | using kinect for face recognition under varying poses, expressions, illumination and disguise |
| url | http://hdl.handle.net/20.500.11937/6810 |