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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 recognition system of our approach.
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f6-sensors-13-04499: The recognition system of our approach.

Mentions: Our approach can be illustrated in Figure 6. First we used a homomorphic filter and histogram specification to obtain an excellent image splicing effect. The Adaboost algorithm with Haar features [23] was applied to catch an accurate facial contour, which prepares for the BG2D2LRP feature extraction. After we have detected the face and resized the detected face to be 120 × 120, the BG3DLRP feature could be extracted from the face. We choose PCA [24] and LDA [25] for dimension reduction because they are useful to enhance the recognition performance of our feature. At last, given two vectors after PCA and LDA translation, we chose cosine similarity [26] to calculate the distance as it can effectively avoid the difference of the same individual in different degree and has better cooperation with BG3DLRP feature.


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

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

The recognition system of our approach.
© Copyright Policy
Related In: Results  -  Collection

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

f6-sensors-13-04499: The recognition system of our approach.
Mentions: Our approach can be illustrated in Figure 6. First we used a homomorphic filter and histogram specification to obtain an excellent image splicing effect. The Adaboost algorithm with Haar features [23] was applied to catch an accurate facial contour, which prepares for the BG2D2LRP feature extraction. After we have detected the face and resized the detected face to be 120 × 120, the BG3DLRP feature could be extracted from the face. We choose PCA [24] and LDA [25] for dimension reduction because they are useful to enhance the recognition performance of our feature. At last, given two vectors after PCA and LDA translation, we chose cosine similarity [26] to calculate the distance as it can effectively avoid the difference of the same individual in different degree and has better cooperation with BG3DLRP feature.

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