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25th International Conference on pattern Recognition (ICPR)

作者： Song, Tiecheng Han, Yuanjing Feng, Jie Wang, Yuanlin Gao, Chenqiang Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China

ISBN: (纸本)9781728188089

local binary pattern (LBP) is sensitive to inverse grayscale changes. Several methods address this problem by mapping each LBP code and its complement to the minimum one. However, without distinguishing LBP codes and their complements, these methods show limited discriminative power. In this paper, we introduce a histogram sorting method to preserve the distribution information of LBP codes and their complements. Based on this method, we propose first- and second-order sorted LBP (SLBP) features which are robust to inverse grayscale changes and image rotation. The proposed method focuses on encoding difference-sign information and it can be generalized to embed other difference-magnitude features to obtain complementary representations. Experiments demonstrate the effectiveness of our method for texture classification under (linear or nonlinear) grayscale-inversion and rotation changes.

关键词： Texture classification illumination invariance rotation invariance local binary pattern

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Face Recognition Using Threshold-Based local binary pattern Resolution of Feature Extraction Problem

SSRN

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SSRN 2024年

作者： JebaRaj, R. Wesley, A. Boyed Department of Computer Science Nesamony Memorial Christian College Affiliated to Manonmaniam Sundaranar University Abishekapatti Tamil Nadu Tirunelveli Marthandam627 012 India

A person's face can reveal a great deal about both their identification and emotional condition. Numerous industries, such as law enforcement, financial and security access control authentication, and personal identification, among others, are impacted by the fascinating and difficult topic of face recognition. This paper introduces a novel and successful face recognition approach based on a threshold-based local binary pattern (LBP) to address the global feature extraction problem with the conventional local binary pattern (LBP). It can increase the system's resistance to noise like light and extract the face's overall characteristics by employing a local binary pattern with Threshold (TLBP). The power of the picture's identification is further increased by combining local and global features from LBP. To decrease dimensions and improve discriminative categorisation, Linear Discriminate Analysis (LDA) and Principal Component Analysis (PCA) are utilized. The TLBP strategy and the LBP methodology are contrasted in this article. The TLBP method, which achieves a 98 per cent recognition accuracy, is tested and examined using the ORL and YALE datasets. © 2024, The Authors. All rights reserved.

关键词： local binary pattern

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FAKE FACE DETECTION USING local binary pattern AND ENSEMBLE MODELING

FAKE FACE DETECTION USING LOCAL BINARY PATTERN AND ENSEMBLE ...

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IEEE International Conference on Image Processing (ICIP)

作者： Wang, Yonghui Zarghami, Vahid Cui, Suxia Prairie View A&M Univ Comp Sci Prairie View TX 77446 USA Prairie View A&M Univ Elect & Comp Engn Prairie View TX USA

ISBN: (纸本)9781665441155

Fake faces generated with Generative Adversarial Networks (GANs) are becoming more and more realistic and getting harder to be identified directly by human beings. However, CNN (Convolutional Neural network) based deep learning architecture can achieve almost perfect detection accuracy on such fake faces. In this paper we present a study of fake face detection with the exploration of the global texture features based on the empirical knowledge that the textures of fake faces are quite different from those of real faces. A new architecture, LBP (local binary pattern)-Net, is designed to utilize binary representation image texture for the effective identification of fake images. Experimental results show that the proposed method is more robust than existing algorithms for detecting fake images edited by different image augmentation methods, such as blurring, cutout, brightness and color changing, equalization, etc. Ensemble models are also experimented to combine advantages of individual models. The most significant effect of ensemble models is the robustness for detecting edited fake images compared to single models. Experimental results show that our ensemble models outperform single models for detecting fake images.

关键词： Fake face detection local binary pattern Ensemble model

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Multi-Channel local binary pattern Guided Convolutional Neural Network for Breast Cancer Classification

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Open Biomedical Engineering Journal 2021年 15卷 132-140页

作者： Mewada, Hiren Al-Asad, Jawad F. Patel, Amit Chaudhari, Jitendra Mahant, Keyur Vala, Alpesh Electrical Engineering Department Prince Mohammad Bin Fahd University Al Khobar Saudi Arabia CHARUSAT Space Research and Technology Center Charotar University of Science and Technology Gujarat Changa India

Background: The advancement in convolutional neural network (CNN) has reduced the burden of experts using the computer-aided diagnosis of human breast cancer. However, most CNN networks use spatial features only. The inherent texture structure present in histopathological images plays an important role in distinguishing malignant tissues. This paper proposes an alternate CNN network that integrates local binary pattern (LBP) based texture information with CNN features. Methods: The study propagates that LBP provides the most robust rotation, and translation-invariant features in comparison with other texture feature extractors. Therefore, a formulation of LBP in context of convolution operation is presented and used in the proposed CNN network. A non-trainable fixed set binary convolutional filters representing LBP features are combined with trainable convolution filters to approximate the response of the convolution layer. A CNN architecture guided by LBP features is used to classify the histopathological images. Result: The network is trained using BreKHis datasets. The use of a fixed set of LBP filters reduces the burden of CNN by minimizing training parameters by a factor of 9. This makes it suitable for the environment with fewer resources. The proposed network obtained 96.46% of maximum accuracy with 98.51% AUC and 97% F1-score. Conclusion: LBP based texture information plays a vital role in cancer image classification. A multi-channel LBP futures fusion is used in the CNN network. The experiment results propagate that the new structure of LBP-guided CNN requires fewer training parameters preserving the capability of the CNN network’s classification accuracy. © 2021 Mewada et al.

关键词： Accuracy Breast cancer Convolutional neural network Histopathological images local binary pattern Medical imaging

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Gradient local binary pattern Layer to Initialize the Convolutional Neural Networks 53

Gradient Local Binary Pattern Layer to Initialize the Convol...

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IEEE International Symposium on Circuits and Systems (IEEE ISCAS)

作者： Jiang, Ning Tang, Jialiang Yu, Wenxin Zhou, Jinjia Mai, Liuwei Southwest Univ Sci & Technol Sch Comp Sci & Technol Mianyang Sichuan Peoples R China Hosei Univ Sch Sci & Engn Tokyo Japan Sichuan Languang Dev Co Ltd Chengdu Peoples R China

ISBN: (纸本)9781728192017

Deep neural network technology is a milestone achievement in the field of computer vision. It obtained the performance that the shallow network cannot achieve through the multi-layer network structure and the learning method of reverse adjustment parameters. However, the feature extraction algorithm of the shallow network is very effective and also is more beneficial for deep neural networks. In this paper, we combine the shallow network algorithm to proposes the gradient local binary pattern layer(GLBP layer) to replace the first layer of Convolutional Neural Networks(CNNs). The GLBP layer plays a role in initializing the CNNs and can improve network performance without increasing the number and complexity of network layers. In the experiment, using the extracted layer modified by the GLBP feature algorithm to replace other classic deep neural networks, 2.65% and 2.9% performance improvements were obtained in the WideResNet16-2 and ResNet-101 respectively when training on CIFAR-100 dataset.

关键词： local binary pattern

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Classification of MRI's images using the Fractional Fourier Transform and the local binary pattern information by Artificial Neural Network 29

Classification of MRI's images using the Fractional Fourier ...

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Conference on Infrared Remote Sensing and Instrumentation XXIX

作者： Estrada Marmolejo, Carlos A. Alvarez-Borrego, Josue Garcia-Torales, Guillermo Flores Nunez, Jorge Luis Univ Guadalajara Av Revoluc 1500 Guadalajara 44430 Jalisco Mexico CICESE Carretera Ensenada Tijuana 3918 Zona Playitas Ensenada 22860 Baja California Mexico

ISBN: (数字)9781510644991

ISBN: (纸本)9781510644991;9781510644984

pattern recognition techniques are widely used in computer vision, classification of radio signals, and voice recognition. The fractional Fourier transform is used to recognize patterns using binary rings masks and segment images. This technique has the characteristic of being invariant to position and rotation and finally obtaining a one-dimensional signature. On the other hand, Neural Networks are used for pattern recognition based on a deep neural network algorithm. It has the characteristic of training large datasets with millions of images. Artificial Neural Networks(ANNs) are used for several applications such as pattern recognition and classification of input data. In particular, the ANN has been used to evaluate medical images from the brain to assess if the image corresponds to Alzheimer's disease. One disadvantage of the neural network is a large amount of time to learn depending on the number of patterns to be identified or classified and the ability to adapt and recognize patterns. Besides, the fractional Fourier transform cannot analyze a large amount of information. In this work, a comparison between the Artificial Neural Network and the Fractional Fourier Transform is presented to determine which will be the best for recognizing a batch of selected medical images. We propose a reconstruction method using both techniques for precise image recognition and the evaluation of their respective metrics such as accuracy, precision, sensitivity, and specificity. The medical images regarding Alzheimer's disease are no dementia, very mild dementia, mild dementia presenting the best perfomance regarding the receiver operating characteristics and moderate dementia was the worst classified related to the number of images of the dataset.

关键词： pattern recognition one-dimensional signature fractional Fourier transform binary rings masks Artificial Neural Network computer vision image processing local binary pattern neural network

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Digital Image Forgery Detection Using local binary pattern (LBP) and Harlick Transform with classification 2

Digital Image Forgery Detection Using Local Binary Pattern (...

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2nd IEEE International Power and Renewable Energy Conference (IPRECON)

作者： Sabeena, M. Abraham, Lizy Varghese, Anju Coll Engn Dept Elect & Commun Karunagappally Kerala India LBS Inst Technol Women Thiruvananthapuram Kerala India APJ Abdul Kalam Technol Univ Thiruvananthapuram Kerala India LBS Inst Technol Women Dept Elect & Commun Thiruvananthapuram Kerala India

ISBN: (纸本)9781665401371

Images are a natural carrier of information. Images are used in an immense range of applications nowadays, including military purposes, surveillance systems, insurance processing, the internet, television, advertising media, forensic investigation, and so on. However, because powerful, low-cost image editing tools are readily available, these images can be easily tampered with. Therefore, the authenticity of images has become questionable. In this age of advanced PC innovation, digital picture and video have high significance in our everyday life. For editing or modifying the original multimedia contents, a range of low-cost multimedia content handling tools, techniques, and applications with various advanced features are available on the Internet, such as Adobe Photoshop. To handle this issue, numerous investigations have been centered around how to identify, this kind of controlled media. Existing computerized fraud identification techniques are grouped into two significant classes: active and passive. This paper proposes a technique of copy-move forgery detection in which feature extraction is done by local binary patterns (LBP) and Harlick features. For the verification of the authenticity of the image various supervised machine learning classifiers like Support Vector Machine (SVM), Random Forest (RF) & Gradient Boost classifiers are used and the performance of forgery detection is based on these classifiers are analyzed.

关键词： Copy move forgery local binary pattern Harlick transform SVM RF GBC

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local binary pattern and its variants: Application to face analysis 1st

Local binary pattern and its variants: Application to face a...

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1st International Conference on Smart Information and Communication Technologies, SmartICT 2019

作者： Lizé, Jade Débordès, Vincent Lu, Hua Kpalma, Kidiyo Ronsin, Joseph Univ Rennes INSA Rennes CNRS IETR - UMR 6164 Rennes35000 France Normal University of Shandong Jinan China

ISBN: (纸本)9783030531867

local binary pattern is a descriptor whose purpose is to summarize the local structure of the images. The goal is to be able to discriminate different images. This method has gone through a large number of changes and adjustments in different types of applications. This paper reviews various LBP methods for facial expression analysis and proposes a new set of variants. Firstly, the principle of LBP is presented and the main variants for face recognition and facial expression analysis are described. Then, new variants are proposed and finally, a comparison with the referred existing ones is made and analyzed through experiments conducted on facial recognition databases YaleB and ORL, and on facial expressions recognition database JAFFE. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

关键词： local binary pattern

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Content-based image retrieval using color and a novel texture descriptor: orientational-based local binary pattern

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第26期34卷

作者： Baarab, Nissrine Chaouki, Brahim E. L. Khalil Masmoudi, Lhoussaine Ibn Zohr Univ Natl Sch Appl Sci ESDS Lab Agadir Morocco Mohammed V Univ Phys Dept LCS Lab Fac Sci Rabat Morocco

The increasing use of images and multimedia has led, in the past years, to a major growth of data and images to store. With this, comes the importance of having a way to facilitate the indexing and the retrieval of the images. In this article, we present a new method for content-based image retrieval. The proposed approach is composed of two phases: first, a preselection of relevant images from the initial database based on HSV color space and using color moment information. The second phase relies on using the selected images from the first phase as a new database and extracting the texture feature using a novel descriptor called orientational-based local binary pattern (OB-LBP) derived from the traditional LBP texture feature technique. In the proposed descriptor (OB-LBP) we compare each neighbor of the center pixel (of 3 x 3 window) with its three closest neighbors and form a binary code of three components for each pixel, then based on their directions we generate three images and produce the histogram to extract the final feature descriptor. Corel-10 k and Wang's databases were used to evaluate the proposed method. The results show a significant improvement in the proposed method over existing methods.

关键词： Content-based image retrieval feature extraction HSV color space local binary pattern texture feature

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Face Detection and Recognition in Real-Time Photos with Haar Cascade and local binary pattern Histogram for Automatic Door Locking System 4

Face Detection and Recognition in Real-Time Photos with Haar...

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4th International Seminar on Photonics, Optics, and Its Applications (ISPhOA)

作者： Amir, Ian Haikal A. Nugroho, Raihan P. Rahayu, Wiwit Ria Febrianty, Dea Faiza Farihah, Nailil Azizah, Winda Nur Setiadi, Iwan Cony Gultom, Yonatan Davidson Inst Teknol Sepuluh Nopember Fac Ind Technol & Syst Engn Dept Engn Phys Photon Engn Lab Surabaya Indonesia

ISBN: (数字)9781510644137

ISBN: (纸本)9781510644137

Home door security systems using facial recognition based on image processing have been widely developed. Face detection in this system uses the Haar-Cascade Method. The system was tested using 4 types of tests, namely the accuracy test, the distance test, the facial expression test, and the lighting conditions test. The results show that this door design system has a total average accuracy level of 97.2%, with an optimal distance of 1.5 meters, and the condition of the lights must be on. Meanwhile, for the facial expression variation test, the system can distinguish well except when the face is tilted left or right.

关键词： local binary pattern

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