Face recognition against illuminations using two directional multi-level threshold-LBP and DCT
In this paper, a new approach named as the Two Directional Multi-level Threshold-LBP Fusion (2D–MTLBP-F) is proposed to solve the problem of face recognition against illuminations. The proposed approach utilizes the Threshold Local Binary Pattern (TLBP) in combination with Discrete Cosine Transform...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Springer
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/72471 |
| _version_ | 1848762759277182976 |
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| author | Alrjebi, M. Liu, Wan-Quan Li, Ling |
| author_facet | Alrjebi, M. Liu, Wan-Quan Li, Ling |
| author_sort | Alrjebi, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, a new approach named as the Two Directional Multi-level Threshold-LBP Fusion (2D–MTLBP-F) is proposed to solve the problem of face recognition against illuminations. The proposed approach utilizes the Threshold Local Binary Pattern (TLBP) in combination with Discrete Cosine Transform (DCT). The utilization of LBP with different thresholds can produce different levels of information, which in turn can be used to improve performance for face recognition against illuminations. First, all images are normalised using a DCT normalisation technique in order to reduce negative effects of noise, blur or illumination. Secondly, the normalised images are transformed into 61 levels of TLBP with thresholds from -30 to 30 and then the normalised DCT image is fused into these TLBP layers as it contains a different type of information in frequency domain. Thirdly, in the training stage, the 2D–MTLBP-F model is trained by searching for the best combination among these 62 layers (61 TLBP +1 DCT image) based on an idea from two dimensional multiple color fusion (2D–MCF). Fourthly, in testing stage for face recognition, all testing and gallery images are transformed into the 2D–MTLBP-F model, and face recognition is performed using the sparse sensing classifier (SRC). Finally, extensive experimental results on five different databases show that the proposed approach has achieved the highest recognition rates in different lighting conditions as well as in uncontrolled environment for FRGC database. In comparison with TLBP and the recently proposed approach of Multi-Scale Logarithm Difference Edge-maps (MSLDE), the proposed approach also achieves much better results on all used datasets. |
| first_indexed | 2025-11-14T10:52:40Z |
| format | Journal Article |
| id | curtin-20.500.11937-72471 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:52:40Z |
| publishDate | 2018 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-724712019-03-06T05:41:11Z Face recognition against illuminations using two directional multi-level threshold-LBP and DCT Alrjebi, M. Liu, Wan-Quan Li, Ling In this paper, a new approach named as the Two Directional Multi-level Threshold-LBP Fusion (2D–MTLBP-F) is proposed to solve the problem of face recognition against illuminations. The proposed approach utilizes the Threshold Local Binary Pattern (TLBP) in combination with Discrete Cosine Transform (DCT). The utilization of LBP with different thresholds can produce different levels of information, which in turn can be used to improve performance for face recognition against illuminations. First, all images are normalised using a DCT normalisation technique in order to reduce negative effects of noise, blur or illumination. Secondly, the normalised images are transformed into 61 levels of TLBP with thresholds from -30 to 30 and then the normalised DCT image is fused into these TLBP layers as it contains a different type of information in frequency domain. Thirdly, in the training stage, the 2D–MTLBP-F model is trained by searching for the best combination among these 62 layers (61 TLBP +1 DCT image) based on an idea from two dimensional multiple color fusion (2D–MCF). Fourthly, in testing stage for face recognition, all testing and gallery images are transformed into the 2D–MTLBP-F model, and face recognition is performed using the sparse sensing classifier (SRC). Finally, extensive experimental results on five different databases show that the proposed approach has achieved the highest recognition rates in different lighting conditions as well as in uncontrolled environment for FRGC database. In comparison with TLBP and the recently proposed approach of Multi-Scale Logarithm Difference Edge-maps (MSLDE), the proposed approach also achieves much better results on all used datasets. 2018 Journal Article http://hdl.handle.net/20.500.11937/72471 10.1007/s11042-018-5812-0 Springer restricted |
| spellingShingle | Alrjebi, M. Liu, Wan-Quan Li, Ling Face recognition against illuminations using two directional multi-level threshold-LBP and DCT |
| title | Face recognition against illuminations using two directional multi-level threshold-LBP and DCT |
| title_full | Face recognition against illuminations using two directional multi-level threshold-LBP and DCT |
| title_fullStr | Face recognition against illuminations using two directional multi-level threshold-LBP and DCT |
| title_full_unstemmed | Face recognition against illuminations using two directional multi-level threshold-LBP and DCT |
| title_short | Face recognition against illuminations using two directional multi-level threshold-LBP and DCT |
| title_sort | face recognition against illuminations using two directional multi-level threshold-lbp and dct |
| url | http://hdl.handle.net/20.500.11937/72471 |