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. 2013 Apr 3;13(4):4499-513.
doi: 10.3390/s130404499.

A high precision feature based on LBP and Gabor theory for face recognition

Wei Xia  1 , Shouyi Yin, Peng Ouyang
Affiliations

A high precision feature based on LBP and Gabor theory for face recognition

Wei Xia et al. Sensors (Basel). .

Abstract

How to describe an image accurately with the most useful information but at the same time the least useless information is a basic problem in the recognition field. In this paper, a novel and high precision feature called BG2D2LRP is proposed, accompanied with a corresponding face recognition system. The feature contains both static texture differences and dynamic contour trends. It is based on Gabor and LBP theory, operated by various kinds of transformations such as block, second derivative, direct orientation, layer and finally fusion in a particular way. Seven well-known face databases such as FRGC, AR, FERET and so on are used to evaluate the veracity and robustness of the proposed feature. A maximum improvement of 29.41% is achieved comparing with other methods. Besides, the ROC curve provides a satisfactory figure. Those experimental results strongly demonstrate the feasibility and superiority of the new feature and method.

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Figures

Figure 1.
Figure 1.
The processing of LBP operators.
Figure 2.
Figure 2.
The circle LBP operators. (Left): R = 1.N = 8; (Middle): R = 2, N = 16; (Right): R = 2, N = 8.
Figure 3.
Figure 3.
The Double Radii LBP model. N = 8, R1 = 2.5, R2 = 1.5. The left is the model and we mark the pixel value of the sampling points in the right.
Figure 4.
Figure 4.
The principle of the layer directed derivative feature P0∼P7.
Figure 5.
Figure 5.
The finally new feature. Each column (P0∼P7) represents the sub-feature at different orientations of the center point. Each row (L1∼L4) represents the final BG2D2LRP feature of the center point.
Figure 6.
Figure 6.
The recognition system of our approach.
Figure 7.
Figure 7.
Databases. The six databases are, from top to bottom in turn: ORL, YALE, ABERDEEN, AR, FSTAR, FRGC, FERET (Reprinted with permission).
Figure 8.
Figure 8.
The ROC curve of different methods on databases. (a–f) in turn: ORL, YALE, AR, ABERDEEN, FRGC, FSTAR and FERET databases. (g) The recognition rate with different radii.
Figure 8.
Figure 8.
The ROC curve of different methods on databases. (a–f) in turn: ORL, YALE, AR, ABERDEEN, FRGC, FSTAR and FERET databases. (g) The recognition rate with different radii.
Figure 8.
Figure 8.
The ROC curve of different methods on databases. (a–f) in turn: ORL, YALE, AR, ABERDEEN, FRGC, FSTAR and FERET databases. (g) The recognition rate with different radii.
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References

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