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    Face recognition against illuminations using two directional multi-level threshold-LBP and DCT

    Access Status
    Fulltext not available
    Authors
    Alrjebi, M.
    Liu, Wan-Quan
    Li, Ling
    Date
    2018
    Type
    Journal Article
    
    Metadata
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    Citation
    Alrjebi, M. and Liu, W. and Li, L. 2018. Face recognition against illuminations using two directional multi-level threshold-LBP and DCT. Multimedia Tools and Applications. 77 (19): pp. 25659-25679.
    Source Title
    Multimedia Tools and Applications
    DOI
    10.1007/s11042-018-5812-0
    ISSN
    1380-7501
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/72471
    Collection
    • Curtin Research Publications
    Abstract

    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.

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