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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 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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f8-sensors-13-04499: 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.

Mentions: We measured three parameters as recognition performance indicators: Recognition Rate; Consuming Time; ROC Curve. The recognition rate is the most intuitive tool to indicate accuracy of the feature. We could get the cost of the feature and the identification system from the time consumption. In addition, the ROC curves illustrate the stability and robustness of the feature and system. The recognition rates are shown in Table 1 and Table 2. We gave the consumed time of each method in Table 3. The ROC curves are shown in Figure 8.


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

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

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.
© Copyright Policy
Related In: Results  -  Collection

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

f8-sensors-13-04499: 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.
Mentions: We measured three parameters as recognition performance indicators: Recognition Rate; Consuming Time; ROC Curve. The recognition rate is the most intuitive tool to indicate accuracy of the feature. We could get the cost of the feature and the identification system from the time consumption. In addition, the ROC curves illustrate the stability and robustness of the feature and system. The recognition rates are shown in Table 1 and Table 2. We gave the consumed time of each method in Table 3. The ROC curves are shown in Figure 8.

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