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Copy-move forgery detection using multiresolution local binary patterns

FORENSIC SCIENCE INTERNATIONAL 2013年第1-3期231卷 61-72页

作者： Davarzani, Reza Yaghmaie, Khashayar Mozaffari, Saeed Tapak, Meysam Semnan Univ Dept Elect & Comp Engn Semnan Iran

Copy-move forgery is one of the most popular tampering artifacts in digital images. In this paper, we present an efficient method for copy-move forgery detection using Multiresolution local binary patterns (MLBP). The proposed method is robust to geometric distortions and illumination variations of duplicated regions. Furthermore, the proposed block-based method recovers parameters of the geometric transformations. First, the image is divided into overlapping blocks and feature vectors for each block are extracted using LBP operators. The feature vectors are sorted based on lexicographical order. Duplicated image blocks are determined in the block matching step using k-d tree for more time reduction. Finally, in order to both determine the parameters of geometric transformations and remove the possible false matches, RANSAC (RANdom SAmple Consensus) algorithm is used. Experimental results show that the proposed approach is able to precisely detect duplicated regions even after distortions such as rotation, scaling, JPEG compression, blurring and noise adding. (c) 2013 Elsevier Ireland Ltd. All rights reserved.

关键词： Digital image forensics Copy-move forgery local binary patterns Duplicated region detection

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A Hybrid Model for Recognizing Human Activity Using Extreme Learning 26

A Hybrid Model for Recognizing Human Activity Using Extreme ...

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26th International Conference on Computer and Information Technology, ICCIT 2023

作者： Rahman, Arafat Nag, Anindya Bairagi, Anupam Kumar Khulna University Computer Science & Engineering Discipline Khulna9208 Bangladesh

ISBN: (纸本)9798350359015

Human activity recognition (HAR) refers to the cognitive process of identifying and labeling various actions individuals perform. HAR has garnered significant attention in recent years due to its multifaceted applications in healthcare, security, human-computer interaction domains, etc. Despite several works for recognizing HAR, this still needs to be improved due to its real-time implications, complex environment, and privacy and ethical issues. This study aims to develop a model for HAR that can accurately differentiate and categorize different human actions from sequences of video images. For this purpose, a local binary Pattern (LBP)-based Extreme Learning Machine (ELM) is utilized. Experimental results show the quality of the proposed model over other comparing models in terms of accuracy, precision, recall, and F1-score. © 2023 IEEE.

关键词： Extreme Learning HAR local binary patterns Support vector machine

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Robust local oriented patterns for ear recognition

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第41-42期79卷 31183-31204页

作者： Hassaballah, M. Alshazly, H. A. Ali, Abdelmgeid A. South Valley Univ Fac Comp & Informat Dept Comp Sci Qena Egypt South Valley Univ Fac Sci Dept Math Qena 83523 Egypt Minia Univ Fac Sci Dept Comp Sci Al Minya 61519 Egypt

Extraction and description of image features is an active research topic and important for several applications of computer vision field. This paper presents a new noise-tolerant and rotation-invariant local feature descriptor called robust local oriented patterns (RLOP). The proposed descriptor extracts local image structures utilizing edge directional information to provide rotation-invariant patterns and to be effective in noise and changing illumination situations. This is achieved by a non-linear amalgamation of two specific strategies;binarizing neighborhood pixels of a patch and assigning binomial weights in the same formula. In the encoding methodology, the neighboring pixels is binarized with respect to the mean value of pixels in an image patch of size 3 x 3 instead of the center pixel. Thus, the obtained codes are rotation-invariant and more robust against noise and other non-monotonic grayscale variations. Ear recognition is considered as the representative problem, where the ear involves localized patterns and textures. The proposed descriptor encodes all images' pixels and the resulting RLOP-encoded image is divided into several regions. Histograms of the regions are constructed to estimate the distribution of features. Then, all histograms are concatenated together to form the final descriptor. The robustness and effectiveness of the proposed descriptor are evaluated through conducting several identification and verification experiments on four different ear databases: IIT Delhi-I, IIT Delhi-II, AMI, and AWE. It is observed that the proposed descriptor outperforms the state-of-the-art texture based approaches achieving a recognition rate of 98% on the average providing the best performance among the tested descriptors.

关键词： Biometrics Ear recognition Features extraction local image descriptors local binary patterns

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Fast and Accurate Detection of Masked Faces Using CNNs and LBPs

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Computer Systems Science & Engineering 2023年第12期47卷 2939-2952页

作者： Sarah M.Alhammad Doaa Sami Khafaga Aya Y.Hamed Osama El-Koumy Ehab R.Mohamed Khalid M.Hosny Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science Department Sinai UniversityArishNorth SinaiEgypt Department of Information Technology Faculty of Computers and InformaticsZagazig UniversityZagazigEgypt

Face mask detection has several applications,including real-time surveillance,biometrics,*** face masks is also helpful for crowd control and ensuring people wear them *** monitoring personnel,it is impossible to ensure that people wear face masks;automated systems are a much superior option for face mask detection and *** paper introduces a simple and efficient approach for masked face *** architecture of the proposed approach is very straightforward;it combines deep learning and local binary patterns to extract features and classify themasmasked or *** proposed systemrequires hardware withminimal power consumption compared to state-of-the-art deep learning *** proposed system maintains two *** first,this work extracted the local features of an image by using a local binary pattern descriptor,and then we used deep learning to extract global *** proposed approach has achieved excellent accuracy and high *** performance of the proposed method was tested on three benchmark datasets:the realworld masked faces dataset(RMFD),the simulated masked faces dataset(SMFD),and labeled faces in the wild(LFW).Performancemetrics for the proposed technique weremeasured in terms of accuracy,precision,recall,and *** indicated the efficiency of the proposed technique,providing accuracies of 99.86%,99.98%,and 100%for RMFD,SMFD,and LFW,***,the proposed method outperformed state-of-the-art deep learning methods in the recent bibliography for the same problem under study and on the same evaluation datasets.

关键词： Convolutional neural networks face mask detection local binary patterns deep learning computer vision social protection Keras OpenCV TensorFlow Viola-Jones

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Features extraction using encoded local binary pattern for detection and grading diabetic retinopathy

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HEALTH INFORMATION SCIENCE AND SYSTEMS 2022年第1期10卷 14页

作者： Berbar, Mohamed A. Menoufia Univ Fac Elect Engn Dept Comp Engn & Sci Menoufia 32952 Egypt

Introduction Reliable computer diagnosis of diabetic retinopathy (DR) is needed to rescue many with diabetes who may be under threat of blindness. This research aims to detect the presence of diabetic retinopathy in fundus images and grade the disease severity without lesion segmentation. Methods To ensure that the fundus images are in a standard state of brightness, a series of preprocessing steps have been applied to the green channel image using histogram matching and a median filter. Then, contrast-limited adaptive histogram equalisation is performed, followed by the unsharp filter. The preprocessed image is divided into small blocks, and then each block is processed to extract uniform local binary patterns (LBPs) features. The extracted features are encoded, and the feature size is reduced to 3.5 percent of its original size. Classifiers like Support Vector Machine (SVM) and a proposed CNN model were used to classify retinal fundus images. The classification is abnormal or normal and to grade the severity of DR. Results Our feature extraction method was tested on a binary classifier and resulted in an accuracy of 98.37% and 98.84% on the Messidor2 and EyePACS databases, respectively. The proposed system could grade DR severity into three grades (0: no DR, 1: mild DR, and 5: moderate, severe NPDR, and PDR). It obtains an F1-score of 0.9617 and an accuracy of 95.37% on the EyePACS database, and an F1-score of 0.9860 and an accuracy of 97.57% on the Messidor2 database. The resultant values are dependent on the selection of (neighbours, radius) pairs during the extraction of LBP features. Conclusions This study's results proved that the preprocessing steps are significant and had a great effect on highlighting image features. The novel method of stacking and encoding the LBP values in the feature vector greatly affects results when using SVM or CNN for classification. The proposed system outperforms the state of the artwork. The proposed CNN model performs better t

关键词： Diabetic retinopathy Histogram matching local binary patterns Fundus image SVM CNN

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On the detection of morphing attacks generated by GANs 21

On the detection of morphing attacks generated by GANs

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Annual International Conference of the Biometrics-Special-Interest-Group (BIOSIG) of the of the Gesellschaft-fur0Informatik (GI) e-V

作者： Colbois, Laurent Marcel, Sebastien Idiap Res Inst Biometr Secur & Privacy Martigny Switzerland Univ Lausanne Ecole Sci Criminelles Lausanne Switzerland

ISBN: (数字)9781665476669

ISBN: (纸本)9781665476669

Recent works have demonstrated the feasibility of GAN-based morphing attacks that reach similar success rates as more traditional landmark-based methods. This new type of "deep" morphs might require the development of new adequate detectors to protect face recognition systems. We explore simple deep morph detection baselines based on spectral features and LBP histograms features, as well as on CNN models, both in the intra-dataset and cross-dataset case. We observe that simple LBP-based systems are already quite accurate in the intra-dataset setting, but struggle with generalization, a phenomenon that is partially mitigated by fusing together several of those systems at score-level. We conclude that a pretrained ResNet effective for GAN image detection is the most effective overall, reaching close to perfect accuracy. We note however that LBP-based systems maintain a level of interest : additionally to their lower computational requirements and increased interpretability with respect to CNNs, LBP+ResNet fusions sometimes also showcase increased performance versus ResNet-only, hinting that LBP-based systems can focus on meaningful signal that is not necessarily picked up by the CNN detector.

关键词： morphing attacks deepfakes gan local binary patterns biometrics stylegan

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An Enhanced Facial Expression Recognition Model Using local Feature Fusion of Gabor Wavelets and local Directionality patterns

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INTERNATIONAL JOURNAL OF AMBIENT COMPUTING AND INTELLIGENCE 2020年第1期11卷 48-70页

作者： Bellamkonda, Sivaiah Gopalan, N. P. Natl Inst Technol Dept Comp Applicat Tiruchirappalli Tamil Nadu India

Facial expression analysis and recognition has gained popularity in the last few years for its challenging nature and broad area of applications like HCI, pain detection, operator fatigue detection, surveillance, etc. The key of real-time FER system is exploiting its variety of features extracted from the source image. In this article, three different features viz. local binary pattern, Gabor, and local directionality pattern were exploited to perform feature fusion and two classification algorithms viz. support vector machines and artificial neural networks were used to validate the proposed model on benchmark datasets. The classification accuracy has been improved in the proposed feature fusion of Gabor and LDP features with SVM classifier, recorded an average accuracy of 93.83% on JAFFE, 95.83% on CK and 96.50% on MMI. The recognition rates were compared with the existing studies in the literature and found that the proposed feature fusion model has improved the performance.

关键词： Artificial Neural Networks Facial Emotion Recognition Feature Fusion Gabor Wavelets local binary patterns local Directionality patterns Machine Vision Support Vector Machines

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Automatic Classification of HEp-2 Cells using Multi-Dimensional local binary patterns

Automatic Classification of HEp-2 Cells using Multi-Dimensio...

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2nd IAPR Asian Conference on Pattern Recognition (ACPR)

作者： Doshi, Niraj P. Schaefer, Gerald Univ Loughborough Dept Comp Sci Loughborough Leics England

ISBN: (纸本)9781479921904

Indirect immunolluorescence imaging is a fundamental technique used for detecting antinuclear antibodies in HEp-2 cells. This is particularly useful for the diagnosis of autoimmune diseases and other important pathological conditions involving the immune system. HEp-2 cells can be categorised into six groups: homogeneous, fine speckled, coarse speckled, nucleolar, cytoplasmic, and centromere cells, which give indications of different autoimmune diseases. This categorisation is typically performed through manual evaluation which is time consuming and subjective. In this paper, we propose a method for automatic classification of HEp-2 cells using local binary pattern (LBP) based texture descriptors. LBP is a simple yet powerful texture algorithm which encodes the relationship of pixels to their local neighbourhood. In our approach, we utilise multi-dimensional LBP (MD-LBP) histograms, which record multi-scale texture information while maintaining the relationships between the scales, and perform classification using support vector machines. We demonstrate our algorithm to work well on a dataset of 721 cell images, giving a correct classification rate exceeding 95%, which is particularly impressive as it is based solely on a single type of image feature.

关键词： indirect immunouorescence imaging HEp-2 cell classification texture local binary patterns MD-LBP

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An analysis of monochrome conversions and normalizations on the local binary patterns texture descriptors

An analysis of monochrome conversions and normalizations on ...

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IEEE International Conference on Computer Vision (ICCV)

作者： Nourani-Vatani, Navid De Deuge, Mark Douillard, Bertrand Williams, Stefan B. Univ Sydney Australian Ctr Field Robot Darlington NSW 2050 Australia CALTECH Jet Prop Lab Pasadena CA 91109 USA

ISBN: (纸本)9781479930227

While the importance of the choice of color space for color descriptors has been studied extensively, a similar study for image texture descriptors is missing. This publication investigates the effect of color-to-monochrome conversions, image normalization, and metrics on the discriminative power of texture descriptors. The measure of the discriminative power of a feature is formulated as supervised spectral feature analysis. This analysis allows to measure the relative performance of a feature under varying conditions as long as the feature dimension is maintained. Feature discrimination evaluation is applied to local binary patterns texture descriptors and it is shown how the proposed metric directly maps to classification performance. Based on this metric, we demonstrate that the choice of color-to-monochrome conversion and normalization can have a significant effect on the performance of the LBP descriptors.

关键词： Color space conversions local binary patterns Normalization Spectral Analysis Texture descriptors texture descriptor Spectrum Analysis binary pattern normalization Metronidazole color spaces

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Robust ASL Fingerspelling Recognition Using local binary patterns And Geometric Features

Robust ASL Fingerspelling Recognition Using Local Binary Pat...

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International Conference on Digital Image Computing - Techniques and Applications (DICTA)

作者： Weerasekera, C. S. Jaward, M. H. Kamrani, N. Monash Univ Dept Elect & Comp Syst Engn Clayton Vic Australia

ISBN: (纸本)9781479921263

Sign language recognition using computer vision techniques enables machines to function as interpreters of sign language while eliminating the need for cumbersome data gloves. In this paper, a robust approach for recognition of bare-handed static sign language is presented, using a novel combination of features. These include local binary patterns (LBP) histogram features based on color and depth information, and also geometric features of the hand. Linear binary Support Vector Machine (SVM) classifiers are used for recognition, coupled with template matching in the case of multiple matches. An accurate hand segmentation scheme using the Kinect depth sensor is also presented. The resulting sign language recognition system could be employed in many practical scenarios and works in complex environments in real-time. It is also shown to be robust to changes in distance between the user and camera and can handle possible variations in fingerspelling among different users. The algorithm is tested on two ASL fingerspelling datasets where overall classification rates over 90% are observed.

关键词： Sign Language Recognition local binary patterns Support Vector Machines Microsoft Kinect

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