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9th International Workshop on pattern Recognition

作者： Tiew, Ken Ming Ong, Thian Song Teng, Jackson Horlick Shern, Alvin Kua Chee Faculty of Information Science and Technology Multimedia University Malaysia

ISBN: (数字)9781510685772

ISBN: (纸本)9781510685765

Finger Vein Recognition system is increasingly used for personal recognition. However, unimodal biometrics systems suffer from several limitations, leading to reduced reliability and effectiveness compared to multimodal biometrics systems. This study implements a multi-instance biometric system by leveraging multiple fingers as the input source to enhance the robustness of the finger vein recognition. The works first employs various preprocessing to improve image quality. These techniques include Watershed Segmentation, Morphological Operations, Histogram Equalization, and resizing. local binary pattern (LBP) is then used to extract the features from each finger vein. This study utilizes two fusion methods, including Feature Fusion and local Feature Aggregation (LFA) to combine results from multiple finger sources. An experimental setup is implemented to evaluate the performance of both fusion methods. The experimental result indicates that the proposed system using LFA achieves higher performance with the lowest Equal Error Rate (EER) of 0.22% and 0.25% for the UTFVP dataset and SDUMLA-HMT dataset, respectively. This emphasizes the ability of the LFA to enhance the robustness of the finger vein recognition system, contributing to future research. © 2024 SPIE.

关键词： local binary pattern

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Enhancing Security with Machine Learning-based Finger-Vein Biometric Authentication System 5

Enhancing Security with Machine Learning-based Finger-Vein B...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Babu, G. Prakash Aswal, Upendra Singh Sindhu, M. Upadhye, Vijay Jagdish Natrayan, L. Patil, Harshal Acharya Institute of Technology Department of Computer Science and Engineering Karnataka Bangalore560 107 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India R.M.K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur601 206 India Parul Institute of Applied Sciences Research and Development Cell Parul University Department of Microbiology Gujarat Waghodia391760 India SIMATS Saveetha School of Engineering Department of Mechanical Engineering Tamil Nadu Chennai602105 India Computer Science and Engineering Symbiosis Institute of Technology Maharashtra Pune412 115 India

ISBN: (纸本)9798350395235

Biometric authentication systems have gained popularity as the necessity for secure individual identification has expanded. Iris, hand geometry, fingerprints, retina, vein patterns on the fingers and palms, and voice patterns are all common biometric features. Biometric recognition systems based on finger-veins are becoming more common due to their numerous advantages, including low danger of duplication or fraud, minimum invasiveness, and excellent identification accuracy. Motivated by these advantages, our research looks into the subject of finger-vein authentication. Similar to identification, exploration of hidden patterns within data is essential for effective finger-vein recognition. The study employs two feature extraction approaches, the local binary pattern (LBP) and the Gabor filter, as well as two machine learning models, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Support Vector Machine (SVM). Using finger-vein images from the SDUMLA HMT database, this study evaluates four distinct feature extraction and ML model combinations. The simulation results highlight the excellent accuracy of 97.72%, False Acceptance Rate (FAR) of 3.1%, and False Rejection Rate (FRR) of 1.41% attained by LBP with ANFIS. In addition to performance measurements, the study examines the amount of time required for each task. © 2024 IEEE.

关键词： local binary pattern

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A new deep learning approach for classification of hyperspectral images: feature and decision level fusion of spectral and spatial features in multiscale CNN

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GEOCARTO INTERNATIONAL 2022年第14期37卷 4208-4233页

作者： Sharifi, Obeid Mokhtarzadeh, Mehdi Beirami, Behnam Asghari KN Toosi Univ Technol Fac Geodesy & Geomat Engn Dept Photogrammetry & Remote Sensing Tehran Iran

Classification is the main field of hyperspectral data processing. To date, many methods are introduced to increase the accuracy of image classification. In recent years, various convolutional neural network models are proposed for hyperspectral image classification. This study puts forward a multiscale structure of convolutional neural networks that use several patches of different sizes to extract complex spatial features. Due to spatial features' effectiveness in improving the classification accuracy of hyperspectral images, the proposed framework integrates spatial features of three methods;morphological profiles, Gabor filter, and local binary pattern with spectral features at both the feature-level and decision-level. The experiments on three hyperspectral images, Indian Pine, Pavia University, and NCALM demonstrate the proposed method's efficiency. The final results show that the proposed method's overall classification accuracy is 6% higher than some other recent techniques.

关键词： Hyperspectral image classification convolutional neural networks morphological profiles Gabor filter local binary pattern

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Authentication of Holograms with Mixed patterns by Direct LBP Comparison 25

Authentication of Holograms with Mixed Patterns by Direct LB...

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25th IEEE International Workshop on Multimedia Signal Processing (MMSP)

作者： Chapel, Marie-Neige Al-Ghadi, Musab Burie, Jean-Christophe La Rochelle Univ L3i La Rochelle France

ISBN: (纸本)9798350338935

In order to combat fraud, identity documents and currencies often include security elements such as guilloches, micro prints or holograms. This paper aims to authenticate such documents from videos acquired with a smartphone by analyzing the holograms. The proposed method consists of recognizing all the patterns of the hologram to determine if the document is genuine or not. The local binary patterns (LBP) descriptor is used in this paper to represent the features of a hologram. For a given document, Multi LBP Models are built as a reference model. This model is then compared to the LBP models of the tested hologram to decide if the hologram exist or not in the document and then to determine if the document is genuine or not. Experiments are carried out on holograms of French Passports and Euro banknotes. The results show that the proposed strategy allows to determine if the document is an authentic document or falsified in a good accuracy. The code is available at https://***/mnchapel/authentication_of_holograms_with_mixed_patterns_by_direct_lbp_comparison.

关键词： authentication recognition hologram local binary pattern

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Conventional Machine Learning Approach for Waste Classification 6

Conventional Machine Learning Approach for Waste Classificat...

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6th Artificial Intelligence and Cloud Computing Conference (AICCC) / 5th Asia Digital Image Processing Conference

作者： Jangsamsi, Kharittha King Mongkuts Univ Technol Thonburi Fac Engn Dept Comp Engn Bangkok Thailand

ISBN: (纸本)9798400716225

Waste management is a complex and challenging process, especially waste classification to sort waste by categories. The paper aims to overcome these challenges by proposing a waste classification approach that uses various feature extraction algorithms along with a support vector machine (SVM). The purpose is to identify the most effective feature for building a classification model, even with a low number of samples and high intra-class variance. SVM was used for classification while Fourier descriptors (FDs), histogram of oriented gradients (HOG), and local binary pattern (LBP) were used for feature extraction. The dataset used in this paper was obtained from *** and *** with different types of vision problems. The experimental results showed that classification with LBP feature extraction achieves the highest accuracy. This accuracy is higher than the experiments with other feature extractions.

关键词： waste classification feature extraction Fourier descriptors histogram of oriented gradients local binary pattern

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Sliding Window Based Support Vector Machine System for Classification of Breast Cancer Using Histopathological Microscopic Images

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IETE JOURNAL OF RESEARCH 2022年第1期68卷 59-67页

作者： Alqudah, Amin Alqudah, Ali Mohammad Yarmouk Univ Dept Comp Engn Irbid Jordan Yarmouk Univ Dept Biomed Syst & Informat Engn Irbid Jordan

Breast cancer is one of the most common cancers in women's community, which is responsible for millions of death cases. The early diagnosis of breast cancer in its early stages increases the chances of healing;the most common current diagnosis methods are based on digital mammogram images and histopathological images. Automatic diagnosis of breast cancer and classifying the type of cancer is recommended to decrease the error that can be caused by humans. Recently, many systems have been developed to diagnose breast cancer by extracting textural and non-textural features from digital mammograms or histopathological images. This paper proposes a new sliding window technique for feature extraction where the local binary pattern (LBP) features are used. In this technique, each image produces 25 sliding windows. Features extracted from each window are saved and used to build a Support Vector Machine (SVM) classifier. The SVM classifier is used to classify each image into benign and malignant based on its most common windows classes. The system can be used to localize the cancerous tissues from the whole histopathological image. The proposed method achieved an overall accuracy of 91.12%, sensitivity of 85.22%, and specificity of 94.01%. Which is considered high when compared with other systems in the literature. The system can extend to extract more features and a comparison between different machine learning algorithms can performed.

关键词： Breast cancer Histopathological images local binary pattern Sliding window features Support vector machine

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A novel effectiveness evaluation method of smokescreen jamming based on the fusion of directional gradients and local texture features

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AIP ADVANCES 2022年第11期12卷

作者： Liu, Shuxin Zhao, Feng Jiang, Huhai Wu, Hui Wang, Daihua Chongqing Univ Minist Educ China Key Lab Optoelect Technol & Syst Chongqing 400044 Peoples R China Southwestern Tech Inst Phys Chengdu 610041 Peoples R China

Smokescreen jamming is one of the most common types of interference in optical imaging. However, there are few tools available to assess the effectiveness of optical imaging in the presence of smoke screen interference, and most of them are qualitative in nature. There is an urgent need for targeted quantitative assessment methods. Therefore, a novel effectiveness evaluation method of smoke screen jamming (EEMSSJ) based on the histogram of oriented gradient and local binary pattern feature is proposed. According to the influence on the tracking effect, the proposed method weights and fuses the directional gradient histogram feature, texture feature, cosine similarity, and luminance feature of the image and finally obtains the EEMSSJ index. The EEMSSJ index provides a quantitative assessment of the effectiveness of smoke screen interference from an image perspective, which can effectively improve the accuracy of the analysis and judgment of the smokescreen jamming situation and provide a strong reference basis for making timely adjustments to the interference deployment. The effectiveness of the proposed method and the EEMSSJ index are validated by experiments. Experimental results show that the EEMSSJ index can achieve a high level of agreement with the subjective assessment of the experimental data and possess high sensitivity and robustness. The EEMSSJ index provides a more accurate representation of the dynamic disturbance of the smoke screen than the correlation assessment method and the multi-scale structural similarity method.

关键词： local binary pattern

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Novel and robust color texture descriptors for color face recognition

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第15期81卷 21313-21347页

作者： Singh, Chandan Majeed, Shahbaz Punjabi Univ Dept Comp Sci Patiala 147002 Punjab India

We propose novel and robust color texture descriptors which are based on the relative dominance of the discriminative power of the color components in a multi-channel representation of a color model. LBP-like operators are derived by fitting linear regression models in 2D color subspaces RG, and GB of a 3D RGB color space keeping in view of the relative dominance of G component over the R, and B components. The linear regression models yield three operators LBPLRG, LBPLGB, and LBPLCGB, which are jointly referred to as local binary patterns using lines (LBPL). The features of the three operators are combined to form feature vectors. To further boost the performance, the LBPL features are combined with the existing local binary pattern of color images (LBPC). Experimental results demonstrate the superiority of the proposed operators, LBPL and LBPL + LBPC over the state-of-the-art LBP-, SRC- and under certain conditions over CNN-based approaches across various classifiers and they are found to be robust to many variations in the face images.

关键词： Face recognition Color texture descriptor local binary pattern local binary pattern for color images

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Semi-dense Map Reconstruction of Bronchus Based on Prior Feature Correlation 4

Semi-dense Map Reconstruction of Bronchus Based on Prior Fea...

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4th International Symposium on Intelligent Robotics and Systems, ISoIRS 2024

作者： Ren, Le Yang, Peng Sun, Rongchuan Yu, Shumei Wang, Gang Sun, Lining Soochow University School of Mechanical and Electrical Engineering Suzhou China University of Shanghai for Science and Technology School of Health Science and Engineering China

ISBN: (纸本)9798350364798

Simultaneous localization and mapping (SLAM) technology can perform map visualization and bronchoscopy positioning based on monocular bronchoscopy images, reduce the complexity of doctor 's operation, and improve the success rate and safety of surgery. Aiming at the problems of sparse mapping and uncertain scale caused by complex bronchial anatomical structure and weak intracavity texture, this paper proposes an improved monocular bronchoscopy SLAM algorithm. Inspired by the idea of Kalman filtering, a search and matching strategy combining prior feature correlation and local binary pattern (LBP) is proposed to inverse the depth of some pixels, which improves the density of mapping under the premise of ensuring accuracy. The dynamic experimental platform built by the simulated bronchial model verifies the feasibility of the method. © 2024 IEEE.

关键词： local binary pattern

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Face recognition using color local binary pattern from mutually independent color channels

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EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING 2013年第1期2013卷 1-11页

作者： Anbarjafari, Gholamreza Cyprus Int Univ Dept Elect & Elect Engn KKTC TR-10 Lefkosa Mersin Turkey

In this article, a high performance face recognition system based on local binary pattern (LBP) using the probability distribution functions (PDFs) of pixels in different mutually independent color channels which are robust to frontal homogenous illumination and planer rotation is proposed. The illumination of faces is enhanced by using the state-of-the-art technique which is using discrete wavelet transform and singular value decomposition. After equalization, face images are segmented by using local successive mean quantization transform followed by skin color-based face detection system. Kullback-Leibler distance between the concatenated PDFs of a given face obtained by LBP and the concatenated PDFs of each face in the database is used as a metric in the recognition process. Various decision fusion techniques have been used in order to improve the recognition rate. The proposed system has been tested on the FERET, HP, and Bosphorus face databases. The proposed system is compared with conventional and the state-of-the-art techniques. The recognition rates obtained using FVF approach for FERET database is 99.78% compared with 79.60 and 68.80% for conventional gray-scale LBP and principle component analysis-based face recognition techniques, respectively.

关键词： Illumination robust local binary pattern Face recognition Probability distribution function Discrete wavelet transform Kullback-Leibler distance

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