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Infrared Face Recognition Based on Adaptive Dominant pattern of local binary pattern 1

10th Chinese Conference on Image and Graphics Technologies and Applications (IGTA)

作者： Xie, Zhihua Jiangxi Sci & Technol Normal Univ Key Lab Opt Elect & Commun Nanchang Jiangxi Peoples R China

ISBN: (数字)9783662477915

ISBN: (纸本)9783662477915;9783662477908

Infrared face recognition, being light-independent, and not vulnerable to facial skin, expressions and posture, can avoid or limit the drawbacks of face recognition in visible light. local binary pattern (LBP), as a classic local feature descriptor, is appreciated for infrared face feature representation. To extract compact and principle information from LBP features, infrared face recognition based on LBP adaptive dominant pattern is proposed in this paper. Firstly, LBP operator is applied to infrared face for texture information. Based on the statistical distribution, the variable dominant pattern is attained for different infrared faces. Finally, dissimilarity metrics between the adaptive dominant pattern features is defined for final recognition. The experimental results show the adaptive dominant patterns in infrared face image have a lower feature dimensionality, and the proposed infrared face recognition method outperforms the traditional methods based on LBP uniform and discriminant patterns.

关键词： local binary pattern Adaptive dominant pattern Uniform pattern Dimensionality reduction Infrared face recognition

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An extended center-symmetric local binary pattern for background modeling and subtraction in videos 10

An extended center-symmetric local binary pattern for backgr...

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10th International Conference on Computer Vision Theory and Applications, VISAPP 2015

作者： Silva, Caroline Bouwmans, Thierry Frélicot, Carl Lab. Mathématiques Images et Applications Université de La Rochelle La-Rochelle17000 France

ISBN: (纸本)9789897580895

In this paper, we propose an eXtended Center-Symmetric local binary pattern (XCS-LBP) descriptor for background modeling and subtraction in videos. By combining the strengths of the original LBP and the similar CS ones, it appears to be robust to illumination changes and noise, and produces short histograms, too. The experiments conducted on both synthetic and real videos (from the Background Models Challenge) of outdoor urban scenes under various conditions show that the proposed XCS-LBP outperforms its direct competitors for the background subtraction task. Copyright © 2015 SCITEPRESS - Science and Technology Publications All rights reserved.

关键词： local binary pattern

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local binary pattern orientation based face recognition

Local binary pattern orientation based face recognition

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Y. K. Shen C. T. Chiu Dept. of Comput. Sci. Nat. Tsing Hua Univ. Hsinchu Taiwan

ISBN: (纸本)9781467369985

Scale-invariant feature transform (SIFT) is a feature point based method using the orientation descriptor for pattern recognition. It is robust under the variation of scale and rotation changes, but the computation cost increases with its feature points. local binary pattern (LBP) is a pixel based texture extraction method that achieves high face recognition rate with low computation time. We propose a new descriptor that combines the LBP texture and SIFT orientation information to improve the recognition rate using limited number of interest points. By adding the LBP texture information, we could reduce the SIFT orientation number in the descriptor by half. Therefore, we could reduce the computation time while keeping the recognition rate. In addition, we propose a matching method to reserve the effective matching pairs and calculate the similarity between two images. By combining these two methods, we can extract different face details effectively and further reduce computational cost. We also propose an approach using the region of interest (ROI) to remove the useless interest points for saving our computation time and maintaining the recognition rate. Experimental results demonstrate that our proposed LBP orientation descriptor can reduce around 30% computation time compared with the original SIFT descriptor while maintaining the recognition rate in FERET database. Adding the ROI at our proposed LBP orientation descriptor can reduce around 58% computation time compared with the original SIFT descriptor in FERET database. For extended YaleB database, our method has 1.2% higher recognition rate than original SIFT method and reduces 28.6% computational time. The experimental results with adding ROI reduces 61.9% computation time for YaleB database.

关键词： face recognition local binary pattern scale-invariant feature transform computational time Facial Recognition binary pattern Descriptors LBP gene LBP Feature Points Recognition (Psychology)

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local binary patterns of Segments of a binary Object for Shape Analysis

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JOURNAL OF MATHEMATICAL IMAGING AND VISION 2023年第4期65卷 618-630页

作者： Kumar, Ratnesh Mali, Kalyani Univ Kalyani Dept Comp Sci & Engn Nadia 741235 W Bengal India

The paper presents an effective, robust and geometrically invariants, collection of contours or boundaries base local binary pattern (LBP) for binary object shape retrieval and classification. The contours segmentation or deformations of an object is a preprocessing step of shape retrieval and classification that segment the binary object shape in a shape-preserving sequence of contours segment using a coordination number shape segmentation approach. The proposed local binary pattern extracts the minimum decimal value corresponding to the pattern of object contour points for each and every contours segment. It is one of the most important features in content-based image retrieval. At the matching stage, we find Euclidean distance between eigenvalues of correlation coefficient of Hu's seven moments corresponding to each contour segment for given two objects. The LBP pattern corresponding to the image contour provides excellent power, which is demonstrated by excellent retrieval performance on several popular shape benchmarks, including MPEG-7 CE-Shape-1 dataset and Kimia's dataset. Experimental results obtained from popular databases demonstrate that the proposed linear binary pattern can achieve comparably better results than existing algorithms.

关键词： local binary pattern Eigenvalue Hu's moments Correlation coefficient Coordination number

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SARLBP: Scale Adaptive Robust local binary patterns for Texture Representation

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2025年 34卷 969-981页

作者： Upadhyay, Parth C. Lory, John A. Desouza, Guilherme N. Univ Missouri Dept Elect Engn & Comp Sci EECS Vis Guided & Intelligent Robot ViGIR Lab Columbia MO 65211 USA Univ Missouri Div Plant Sci Columbia MO 65211 USA

local binary pattern (LBP) and its variants have considerable success in a wide range of computer vision and pattern recognition applications, especially in tasks related to texture classification. However, the LBP method is sensitive to noise, scale variations and unable to capture macro-structure information. We propose a novel texture classification descriptor called Scale Adaptive Robust LBP (SARLBP) that enhances macro-level descriptive information by incorporating significantly larger scales, and a novel encoding scheme, which is designed to overcome the limitations of traditional LBP schemes. SARLBP method dynamically determines a single optimal scale for each radial direction from multiple scales based on the local area's characteristics. Subsequently, this descriptor extracts four distinct patterns derived from regional image medians of center pixel, radially-optimized neighbor pixels, optimized fixed scale-based pixels, and radial-difference-based pixels. This method adeptly captures texture information at both micro and macro scales by employing scale adaptation based on the distinctive attributes of the local region. As a result, it provides a comprehensive and robust representation of the texture images. Extensive experimentation was conducted on four publicly available texture databases (ALOT, CUReT, UMD, and Kylberg), considering both the presence and absence of two distinct types of interference (Gaussian noise and Salt-and-Pepper noise). The results reveal that our SARLBP method achieves significantly better performance than other state-of-the-art LPB variants with a fixed smaller feature dimension.

关键词： local binary pattern texture classification scale-adaptive scale-adaptive robust feature extraction robust feature extraction robust feature extraction

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A CNN-Based Indoor Positioning Algorithm for Dark Environments: Integrating local binary patterns and Fast Fourier Transform with the MC4L-IMU Device

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APPLIED SCIENCES-BASEL 2025年第7期15卷 4043-4043页

作者： Yin, Nan Sun, Yuxiang Kim, Jae-Soo Kyungpook Natl Univ Sch Comp Sci & Engn Daegu 41566 South Korea

In our previous study, we proposed a vision-based ranging algorithm (LRA) that utilized a monocular camera with four lasers (MC4L) for indoor positioning in dark environments. The LRA achieved a positioning error within 2.4 cm using a logarithmic regression algorithm to establish a linear relationship between the illuminated area and real distance. However, it cannot distinguish between obstacles and walls. Hence, it results in severe errors in complex environments. To address this limitation, we developed an LBP-CNNs model that combines local binary patterns (LBPs) and self-attention mechanisms. The model effectively identifies obstacles based on the laser reflectivity of different material surfaces. It reduces positioning errors to 1.27 cm and achieves an obstacle recognition accuracy of 92.3%. In this paper, we further enhance LBP-CNNs by combining it with fast Fourier transform (FFT) to create an LBP-FFT-CNNs model that significantly improves the recognition accuracy of obstacles with similar textures to 96.3% and reduces positioning errors to 0.91 cm. In addition, an inertial measurement unit (IMU) is integrated into the MC4L device (MC4L-IMU) to design an inertial-based indoor positioning algorithm. Experimental results show that the LBP-FFT-CNNs model achieves the highest determination coefficient (R2 = 0.9949), outperforming LRA (R2 = 0.9867) and LBP-CNN (R2 = 0.9934). In addition, all models show strong stability, and the prediction standard index (PSI) values are always below 0.02. To evaluate model robustness and MC4L-IMU work reliably under different conditions, the experiments were conducted in a controlled indoor environment with different obstacle materials and lighting conditions.

关键词： local binary pattern fast Fourier transform self-attention mechanism CNNs inertia-based indoor positioning algorithm visual localization obstacle recognition dark environment

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local binary patterns for Classifying Pistachio Species

Local Binary Patterns for Classifying Pistachio Species

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Bao Nguyen, Le Huu Phan, Thi-Thu-Hong FPT University Department of Artificial Intelligence Danang Viet Nam

ISBN: (纸本)9798331530839

To maintain the economic value of pistachio nuts, which is vital in the agricultural economy, efficient post-harvest industrial processes are essential. Different pistachio species cater to distinct markets, increasing the need for accurate classification methods. This study introduces an alternative feature extraction approach using local binary pattern (LBP) to enhance the classification of pistachio species. We apply this method to a dataset comprising 2,148 images of two pistachio species, Kirmizi and Siirt, each with unique market-relevant characteristics. Various machine learning models are evaluated, with Multilayer Perceptron (MLP) emerging as the most effective, achieving a classification accuracy of 96.74%. The findings demonstrate that machine learning methods, combined with LBP, significantly improve the performance and efficiency of pistachio variety classification. These results hold promising applications in precise pistachio selection and intelligent farming techniques. © 2024 IEEE.

关键词： local binary pattern

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Face recognition under complex illumination based on multi-scale weberface and local binary pattern

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Journal of Information and Computational Science 2015年第12期12卷 4585-4593页

作者： Cheng, Shuhong Liu, Jie Wei, Hong Qu, Xiaoman School of Electric Engineering Yanshan University Qinhuangdao China School of Systems Engineering University of Reading Reading United Kingdom

A novel method based on Multi-scale Weberface (MWF) and local binary pattern (LBP) is proposed to reduce illumination effects for face recognition in this paper. The method starts with pre-processing face image by utilizing contrast stretching transformation that reduces the effect of different lighting to some extent. Then different scale Weberfaces are extracted to calculate MWF based on weighted method as multi-scale illumination insensitive face. Finally the study abstracts LBP feature from MWF as face recognition feature under complex illumination and employs multi-class Support Vector Machine (SVM) for face authentication. The experiments are executed both on CMU-PIE and Yale B face databases. The experimental results have indicated that the proposed method can effectively eliminate the influence of face recognition under complex illumination and the recognition rates are superior to Weberface (WF), even single sample images with serious light as training sample images can also work well. Furthermore, this study also has shown that our method is more robust to noise and more suitable to the actual system for face authentication. Copyright © 2015 binary Information Press.

关键词： local binary pattern

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Dual-path fusion network based on transformer and local binary patterns for compressive sensing reconstruction

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第1期36卷 0161a8-0161a8页

作者： Lin, Leping Mao, Jiaqi Ouyang, Ning Guilin Univ Elect & Technol Sch Informat & Commun Guilin Peoples R China

In recent years, deep-learning-based methods have made significant progress in the field of compressed sensing. However, most existing deep-learning-based solutions commonly encounter issues in image reconstruction due to inadequate interaction with image textures during the reconstruction process. In this study, we developed a dual-path fusion network that combined structural and textural information for image reconstruction. Guided by structural priors, we designed a new focus linear cross window transformer network that computes attention through parallel cross-SWs of different sizes, integrating both local and global structural information to enhance feature interaction. Meanwhile, a submodule based on local binary pattern is used to leverage image texture priors for learning texture features, thus providing rich texture information for image reconstruction. The features from these two distinct paths are adaptively fused, and the reconstruction results are optimized using an iterative thresholding algorithm. This method effectively combines the advantages of traditional algorithms and deep neural networks, taking advantage of the structural and textural prior information. Experiments demonstrated that the proposed method significantly improves the quality of image reconstruction and robustness to noise.

关键词： compressed sensing prior local binary pattern transformer

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local binary patterns-Based Retinal Disease Screening 4th

Local Binary Patterns-Based Retinal Disease Screening

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4th International Conference on Data, Engineering, and Applications, IDEA 2022

作者： Shalini, M. Angel Manimaran, M. Rajan, R. Rajbabu, S. Sangeerthana, S. Gokul, K.V. Hindusthan College of Arts and Science Tamilnadu Coimbatore India VIT Bhopal University Madhya Pradesh Bhopal India

ISBN: (纸本)9789819700363

Diabetes develops when the body’s insulin production is improperly controlled by the blood sugar level (glucose). Diabetic retinopathy is caused by the effects of diabetes on the eye. One of the difficult diabetes conditions that can result in blindness is diabetic retinopathy. It is metabolic, and people with the ailment do not notice any symptoms until the disease is well along. So it is important to guarantee early discovery and appropriate care. Different automated systems have been created to do this. Finding a micro-aneurysm in the eye's funds is a crucial aspect of identifying diabetic retinopathy. In order to distinguish between diseased and healthy pictures, this study looks at the texture of fund us images’ discrimination skills. The effectiveness of as a textural descriptor for retinal images, local binary patterns (LBP) investigated for this purpose. By examining the retina's surface texture backdrop and eliminating a preceding stage of lesion segmentation, the objective is to discriminate between normal and diabetic retinopathy (DR) funds pictures. To boost the contrast of the input picture, I suggest processing methods before processing, such as contrast limited adaptive histogram equalization (CLAHE), and to improve red lesion detection, here extract the candidate lens like the circular Hough transform. The resulting picture was finally divided into two categories: both typical and diabetic retinopathy (DR). These findings imply which the technique described that is study is a reliable definition of the retina's texture by an algorithm that it can assist in the early identification of retinal disorders using said technique. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： local binary pattern

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