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

Xia W, Yin S, Ouyang P - Sensors (Basel) (2013)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Tsinghua Center for Mobile Computing, Institute of Microelectronics, Tsinghua University, Beijing 100084, China. maxiaola@gmail.com

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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Related in: MedlinePlus

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.
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f3-sensors-13-04499: 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.

Mentions: First we chose a region that contains double circles both centered in a same point but with different radii. Each circle performs as mentioned in Section 2 and here we give N a constant value 8. The radii are variable, with different values and proportions. We use C1 and R1 to represent the outer circle and the corresponding radius, while C2 and R2 represent the inner circle and the corresponding radius. Figure 3 shows the Double Radii LBP model.


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

Xia W, Yin S, Ouyang P - Sensors (Basel) (2013)

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.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3673096&req=5

f3-sensors-13-04499: 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.
Mentions: First we chose a region that contains double circles both centered in a same point but with different radii. Each circle performs as mentioned in Section 2 and here we give N a constant value 8. The radii are variable, with different values and proportions. We use C1 and R1 to represent the outer circle and the corresponding radius, while C2 and R2 represent the inner circle and the corresponding radius. Figure 3 shows the Double Radii LBP model.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Tsinghua Center for Mobile Computing, Institute of Microelectronics, Tsinghua University, Beijing 100084, China. maxiaola@gmail.com

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.

Show MeSH
Related in: MedlinePlus