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

机译：使用两个方向的多级阈值LBP和DCT针对照明进行人脸识别

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摘要

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.
机译：本文提出了一种新的方法，称为双向多级阈值-LBP融合（2D-MTLBP-F），以解决针对光照的人脸识别问题。提出的方法利用阈值局部二进制模式（TLBP）结合离散余弦变换（DCT）。使用具有不同阈值的LBP可以产生不同级别的信息，而这些信息又可以用于提高针对光照的面部识别的性能。首先，使用DCT归一化技术对所有图像进行归一化，以减少噪声，模糊或照明的负面影响。其次，将归一化的图像转换为阈值从-30到30的61个级别的TLBP，然后将归一化的DCT图像融合到这些TLBP层中，因为它在频域中包含不同类型的信息。第三，在训练阶段，基于二维多维颜色融合（2D-MCF）的思想，通过在这62层（61 TLBP +1 DCT图像）中搜索最佳组合来训练2D-MTLBP-F模型。第四，在人脸识别测试阶段，将所有测试图像和画廊图像转换为2D-MTLBP-F模型，并使用稀疏感知分类器（SRC）进行人脸识别。最后，在五个不同数据库上的大量实验结果表明，对于FRGC数据库，该方法在不同光照条件下以及在不受控制的环境中均实现了最高识别率。与TLBP和最近提出的多尺度对数差异边缘图（MSLDE）方法相比，该方法还可以在所有使用的数据集上获得更好的结果。

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