Neighborhood and center difference-based-LBP for face recognition

Karanwal, Shekhar; Diwakar, Manoj

doi:10.1007/s10044-020-00948-8

Cited by 40 publications

(10 citation statements)

References 58 publications

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“…Also, two standards for testing generalized facial recognition are proposed. Experiments of our criteria confirm that our approach is generalizable as compared to a variety of baselines and other cuttingedge technology [14]. A new face recognition descriptor is the orthogonally segmented local binary sequence descriptor (OD-LBP).…”

Section: Literature Reviewmentioning

confidence: 66%

Descriptor feature based on local binary pattern for face classification

Sahy¹

2022

PEN

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Local Binary Patterns (LBP) is a non-parametric descriptor whose purpose is to effectively summarize local image configurations. It has generated increasing interest in many aspects including facial image analysis, vision detection, facial expression analysis, demographic classification, etc. in recent years and has proven useful in various applications. This paper presents a local binary pattern based face recognition (LBP) technology using a Vector Support Machine (SVM). Combine the local characteristics of LBP with universal characteristics so that the general picture characteristics are more robust. To reduce dimension and maximize discrimination, super vector machines (SVM) are used. Screened and Evaluated (FAR), FARR and Accuracy Score (Acc), not only on the Yale Face database but also on the expanded Yale Face Database B datasets, the test results indicate that the approach is accurate and practical, and gives a recognition rate of 98 %.

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Section: Literature Reviewmentioning

confidence: 66%

Descriptor feature based on local binary pattern for face classification

Sahy¹

2022

PEN

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“…HELBP [37] secures the ACC of [88.12%, 97.77% 98.70%] when TG dt = 1:3. HE-ICA [38], None-ICA [38], NCDB-LBPac [39] and NCDB-LBPc [39] secured the ACC of 72.00%, 65.60%, 98.28% and 97.65% on TG dt = 1. The proposed ROM-LBP outstrip ACC of 9 techniques entirely.…”

Section: Yb Datasetmentioning

confidence: 92%

“…On YB, 11 techniques are picked and compared with ROM-LBP. The detailed description of these are as follows: 2D-DWT+LBP [33], 2D-DWT+HELBP [35] NA NA NA 92.00 MWEE-C [36] N/A N/A N/A 99.80 MEESRC [36] N/A N/A N/A 96.50 HQ [36] N/A N/A N/A 95.70 MBP [24] N/A N/A 52.75 N/A 6x6 MB-LBP [24] N/A N/A 69.25 N/A ELBP [24] N ACC in % 2D-DWT+LBP [33] N/A N/A N/A 93.66 2D-DWT+HELBP [33] N/A N/A N/A 93.66 2D-DWT+MBP [33] N/A N/A N/A 92.50 LC-LBP [24] N/A N/A N/A 85.16 MRELBP-NI [24] N/A N/A N/A 93.00 tLBP [24] N/A N/A N/A 88.66 HELBP [37] 88.12 97.77 98.70 N/A HE-ICA [38] 72.00 N/A N/A N/A None-ICA [38] 65.60 N/A N/A N/A NCDB-LBPac [39] 98.28 N/A N/A N/A NCDB-LBPac [39] 97.65 N/A N/A N/A ROM-LBP 92.18 99.21 99.67 100 NA-Not Available [33], 2D-DWT+MBP [33], LC-LBP [24], MRELBP-NI [24] and tLBP [24] attain the ACC of 93.66%, 93.66%, 92.50%, 85.16%, 93.00% and 88.66% when TG dt = 5. HELBP [37] secures the ACC of [88.12%, 97.77% 98.70%] when TG dt = 1:3.…”

Section: Yb Datasetmentioning

confidence: 99%

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Radial Orthogonal Median LBP (ROM-LBP): A Discriminant Local Descriptor in Light Variations for Face Recognition

Karanwal

Diwakar

2023

JGEU

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LBP and majority of its variants performs extremely well in front of moderate light variations. But when light variations becomes severe then performance of LBP and its variants is not satisfactory. Therefore there is a need of the more promising and impressive descriptor which performs well in harsh light variations. To complement these LBP based descriptors the proposed work launches the novel descriptor for Face Recognition (FR) in harsh lightning variations. This proposed descriptor is called as Radial Orthogonal Median LBP (ROM-LBP). The main demerit of these LBP based descriptors is that they all consider the uniform coordination between the neighbors and center pixel. Which mean raw pixel intensity is used for the comparison with the center pixel. The proposed work eliminates this problem in the introduced descriptor ROM-LBP, by replacing the raw pixels intensity with the median of the radial points in each orthogonal position of the two separate groups. The generated median is then used for comparison with the center pixel. The respective codes obtained from both the groups are concatenated to form the ROM-LBP size. As region feature extraction is done therefore ROM-LBP develops the large feature size. To make more effective descriptor, the services of FLDA is used and then classification was conducted by SVMs. Experiments conducted on EYB and YB datasets demonstrates the ability of the proposed ROM-LBP against various LBP and non-LBP based descriptors.

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“…The LBP technique, was proposed in [13] for texture description and has the advantage of being robust and simple. LBP continues to be used for face recognition as it gives very good results compared to other techniques, while it has improved its performance many times over [14]. Moreover, with the increasing use of machine learning techniques, researchers have integrated algorithms such as LBP and ensemble ad boosted strategy to achieve accurate results in face recognition.…”

Section: Introductionmentioning

confidence: 99%

A Novel Feature Extraction Descriptor for Face Recognition

Salamh

Akyüz

2022

Eng. Technol. Appl. Sci. Res.

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This paper presents a new feature extraction technique for face recognition. The new model, called multi-descriptor, is based on the well-known method of local binary patterns. It involves many different neighborhoods of the central pixel. Its unique advantage is that this descriptor allows the use of different neighborhood sizes instead of only one point. This structure ensures reasonable effectiveness and also provides the possibility to obtain a different distribution of features. Based on the new descriptor, a face recognition model using the pairwise feature descriptor based on the proposed descriptor was developed in this work, and local binary patterns were created to investigate the similarity and dissimilarity between the two models. For both models, the training was done using the support vector machine method on different face databases to overcome face recognition problems such as camera distance, expression, large head size, and illumination variations. The proposed technique achieved perfect accuracy on almost all tested databases including the Extended Yale B and Grimace database.

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Neighborhood and center difference-based-LBP for face recognition

Cited by 40 publications

References 58 publications

Descriptor feature based on local binary pattern for face classification

Descriptor feature based on local binary pattern for face classification

Radial Orthogonal Median LBP (ROM-LBP): A Discriminant Local Descriptor in Light Variations for Face Recognition

A Novel Feature Extraction Descriptor for Face Recognition

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