OD-LBP: Orthogonal difference-local binary pattern for Face Recognition

Karanwal, Shekhar; Diwakar, Manoj

doi:10.1016/j.dsp.2020.102948

Cited by 62 publications

(19 citation statements)

References 35 publications

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“…The image will be disturbed by noise during acquisition, resulting in some noise points in the face image, which will reduce the image definition. Therefore, noise filtering is required [22][23][24]. A median filtering method is used for image denoising.…”

Section: Fast Face Recognition Methods Based Onmentioning

confidence: 99%

Research on Fast Face Recognition Method Based on Double Decision Subspace

Wang

Zhang

Huang

2022

Wireless Communications and Mobile Computing

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Because a large number of labeled face data samples in special scenes need a large number of training samples with identity markers, and it is impossible to accurately extract the characteristics of small samples, a fast face recognition method based on double decision subspace is proposed. A feature recognition structure based on double decision subspace is constructed to preprocess the face image and separate the local features of several corresponding face images. The local binary pattern is used to extract the local texture features of the face, and the deep convolution network face fast recognition model is constructed. The convolution network is used to share the weight, pool, and downsampling to reduce the complexity of the model. The constructed recognition model is used to recognize the features of the face image, and the fast face recognition is effectively completed. The experimental results show that the designed method has high recognition accuracy, less average recognition time, and good recognition performance.

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Section: Fast Face Recognition Methods Based Onmentioning

confidence: 99%

Research on Fast Face Recognition Method Based on Double Decision Subspace

Wang

Zhang

Huang

2022

Wireless Communications and Mobile Computing

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“…The proposed descriptor (OD-LBP) has a beneficial effect on all five databases, outperforming the following ten descriptors: LBP, CS-LBP, MB-LBP, OC-LBP, MBP, HOG, ELBP, and OS-LTP. WCLBP and RLBP were found in most of the subgroups [15]. Differences between facial recognition in the suburbs and in the city (in French) LBP is a relatively new local variation of LBP (NCDB-LBP).…”

Section: Literature Reviewmentioning

confidence: 99%

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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“…In recent years, LBP (local binary pattern) has been widely used in many fields, such as texture classification [25][26][27] and face analysis [28][29][30]. A local binary pattern is an operator used to describe the local texture features of an image, which has gray invariance.…”

Section: Lbp (Local Binary Pattern)mentioning

confidence: 99%

LBP‐based progressive feature aggregation network for low‐light image enhancement

Hua

2021

IET Image Processing

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At night or in other low-illumination environments, optical imaging devices cannot capture details and color information in images accurately because of the reduced number of photons captured and the low signal-to-noise ratio. Consequently, the image is very noisy with low contrast and inaccurate color information, which affects human visual perception and creates significant challenges in computer vision tasks. Low-light image enhancement has great research value because it aims to reduce image noise and improve image quality. In this study, we propose an LBP-based progressive feature aggregation network (P-FANet) for low-light image enhancement. The LBP feature has insensitivity to illumination, and it contains rich texture information. In the network, we input the LBP feature into each iteration of the network in an accompanying manner, which helps to restore some detailed information of the low-light image. First, we input the low-light image into the dual attention mechanism model to extract global features. Second, the extracted different features enter the feature aggregation module (FAM) for feature fusion. Third, we use the recurrent layer to share the features extracted at different stages, and use the residual layer to further extract deeper features. Finally, the enhanced image is output. The rationality of the method in this study has been verified through ablation experiments. Many experimental results show that the method in this study has greater advantages in subjective and objective evaluations compared with many other advanced methods.

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OD-LBP: Orthogonal difference-local binary pattern for Face Recognition

Cited by 62 publications

References 35 publications

Research on Fast Face Recognition Method Based on Double Decision Subspace

Research on Fast Face Recognition Method Based on Double Decision Subspace

Descriptor feature based on local binary pattern for face classification

LBP‐based progressive feature aggregation network for low‐light image enhancement

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