Face Recognition with Local Line Binary Pattern

Petpon, Amnart; Srisuk, Sanun

doi:10.1109/icig.2009.123

Cited by 58 publications

(43 citation statements)

References 12 publications

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“…To make the evaluation convincing, we implement the approaches in [2,[4][5][6][7] and name them as the LBP, LLBP, LTP, Local Directional Pattern and LDP respectively. In the experiments, the nearest neighborhood classifier is used and the Chi-square distance is used as distance measure because all the methods apply spatial histograms as feature representation.…”

Section: Experiments and Discussionmentioning

confidence: 99%

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Local Line Derivative Pattern for face recognition

Lian

Cong

2012

2012 19th IEEE International Conference on Image Processing

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In this paper, we propose a novel face descriptor for face recognition, named Local Line Derivative Pattern (LLDP). High-order derivative images in two directions are obtained by convolving original images with Sobel Masks. A revised binary coding function is proposed and three standards on arranging the weights are also proposed. Based on the standards, the weights of a line neighborhood in two directions are arranged. The LLDP labels in two directions are calculated with the proposed binary coding function and weights. The labeled image is divided into blocks where spatial histograms are extracted separately and concatenated into an entire histogram as features for recognition. The experiments on the FERET and Extended Yale B show superior performances of the proposed LLDP compared to other existing methods based on the LBP. The results prove that the LLDP has good robustness against expression, illumination and aging variations.Index Terms-Face recognition, High-order local pattern, Local binary pattern.

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Section: Experiments and Discussionmentioning

confidence: 99%

“…Thus, the string of ILBP has one more bit compared to the LBP with the same shape and size. Petpon and Srisuk [4] proposed local line binary pattern (LLBP). The LLBP applies a straight line as its neighborhood shape instead of a square or circle shape in the LBP.…”

Section: Introductionmentioning

confidence: 99%

Local Line Derivative Pattern for face recognition

Lian

Cong

2012

2012 19th IEEE International Conference on Image Processing

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“…Though its theory is simple, it is powerful in texture recognition. In recent years, LBP is used widely in texture analysis, 7) face recognition 8,9) and image matching. 10) However, it still has shortcomings in specific application.…”

Section: Introductionmentioning

confidence: 99%

“…This method can capture anisotropic information, which is more general than standard LBP. Reference 9) introduces new parameters:…”

Section: Introductionmentioning

confidence: 99%

Invariant Feature Extraction Method Based on Smoothed Local Binary Pattern for Strip Steel Surface Defect

Chu

Gong

2015

ISIJ International

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An invariant feature extraction method based on smoothed local binary pattern (SLBP) is proposed for strip steel surface defect images. SLBP proposed in this paper is a developed version of local binary pattern (LBP). It is determined by the sign of the difference between weighted grays in local neighborhood. SLBP has the ability of noise smoothing. In this paper, invariant features are obtained by concentric discrete square sampling template (CDSST). Firstly, defect images are resampled on CDSST by the way of coordinate mapping. Then, invariant features in scale, rotation, illumination and translation are extracted by combining two types of SLBP images and gray-level co-occurrence matrix. Experimental results show that this novel feature extraction method not only can extract features with scale, rotation, illumination and translation invariance, but also can effectively suppress noise and maintain high classification accuracy.

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“…The Local Binary Pattern (LBP) operator, a well recognized texture feature has also been acting as a major role in face recognition application [8]- [10]. Texture features combined with geometrical features were proved to be successful in enhancing the quality of face recognition [11].…”

Section: Introductionmentioning

confidence: 99%

Face Representation and Face Recognition using Optimized Local Ternary Patterns (OLTP)

Raja¹,

Sadasivam²

2017

Journal of Electrical Engineering and Technology

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-For many years, researchers in face description area have been representing and recognizing faces based on different methods that include subspace discriminant analysis, statistical learning and non-statistics based approach etc. But still automatic face recognition remains an interesting but challenging problem. This paper presents a novel and efficient face image representation method based on Optimized Local Ternary Pattern (OLTP) texture features. The face image is divided into several regions from which the OLTP texture feature distributions are extracted and concatenated into a feature vector that can act as face descriptor. The recognition is performed using nearest neighbor classification method with Chi-square distance as a similarity measure. Extensive experimental results on Yale B, ORL and AR face databases show that OLTP consistently performs much better than other well recognized texture models for face recognition.

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Face Recognition with Local Line Binary Pattern

Cited by 58 publications

References 12 publications

Local Line Derivative Pattern for face recognition

Local Line Derivative Pattern for face recognition

Invariant Feature Extraction Method Based on Smoothed Local Binary Pattern for Strip Steel Surface Defect

Face Representation and Face Recognition using Optimized Local Ternary Patterns (OLTP)

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