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10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

作者： Mukherjee, Moumita Meenpal, Toshanlal Natl Inst Technol Elect & Telecommun Engn Raipur Madhya Pradesh India

ISBN: (纸本)9781538659069

Automatic kinship verification system is designed to verify kinship relations between given pair of face images. Child adopts many characteristic such as similarity in appearance, likes and dislike, behavior, voice from his/her parents due to overlapping of genes. There are various existing algorithms that can verify whether a given pair of face images share kinship relation. This paper proposes a new method based on compound local binary pattern (CLBP) and local feature-based discriminate analysis (LFDA) to improve kinship verification accuracy. A well known texture feature extraction method is local binary pattern (LBP), but LBP performance deteriorates in flat images. To overcome this drawback, extraction of texture features from face images are calculated by compound local binary pattern (CLBP) technique. Extracted features mainly represent facial characteristics but may also contain some noises. Further, local feature-based discriminate analysis (LFDA) is used as a feature selection method to reduce these noises and choose the most relevant facial features. LFDA reduces inter-class similarity and increases intra-class similarity. Proposed method uses KNN classifier with 5-fold cross-validation. Kinship images are collected from KinFaceW-I and KinFaceW-II dataset. Best mean accuracy for proposed method on KinFaceW-I and KinFaceW-II are 82.825 % and 89.36% respectively. Experimental results also outperforms existing methods on these datasets.

关键词： Kinship verification local binary pattern compound local binary pattern local feature-based discriminate analysis

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compound local binary pattern and Enhanced Jaya Optimized Extreme Learning Machine for Digital Mammogram Classification 1

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19th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

作者： Mohanty, Figlu Rup, Suvendu Dash, Bodhisattva Int Inst Informat Technol Image & Video Proc Lab Bhubaneswar Odisha India

ISBN: (数字)9783030034931

ISBN: (纸本)9783030034931;9783030034924

The fatality rate due to breast cancer still continues to remain high across the world and women are the frequent sufferers of this cancer. Mammography is one of the powerful imaging modalities to detect and diagnose cancer at its early stage effectively. A computer-aided diagnosis (CAD) system is a potential tool which analyses the mammographic images to reach a correct decision. The present work aims at developing a CAD framework which can classify the mammograms accurately. This work has primarily four stages. First, contrast limited adaptive histogram equalization (CLAHE) is used for pre-processing. Second, feature extraction is realized using compound local binary pattern (CLBP) followed by principal component analysis (PCA) for feature reduction. Finally, an enhanced Jaya-based extreme learning machine is utilized to classify the mammograms as normal or abnormal, and further, benign or malignant. The success rate in terms of classification accuracy achieves 100% and 99.48% for MIAS and DDSM datasets, respectively.

关键词： Mammography Computer-aided diagnosis compound local binary pattern Jaya algorithm Extreme learning machine

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An Improved CAD Framework for Digital Mammogram Classification Using compound local binary pattern and Chaotic Whale Optimization-Based Kernel Extreme Learning Machine 27th

An Improved CAD Framework for Digital Mammogram Classificati...

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27th International Conference on Artificial Neural Networks (ICANN)

作者： Mohanty, Figlu Rup, Suvendu Dash, Bodhisattva IIIT Bhubaneswar Image & Video Proc Lab Bhubaneswar Odisha India

ISBN: (纸本)9783030014216

The morbidity and mortality rate of breast cancer still continues to remain high among women across the world. This figure can be reduced if the cancer is identified at its early stage. A Computer-aided diagnosis (CAD) system is an efficient computerized tool used to analyze the mammograms for finding cancer in the breast and to reach a decision with maximum accuracy. The presented work aims at developing a CAD model which can classify the mammograms as normal or abnormal, and further, benign or malignant accurately. In the present model, CLAHE is used for image pre-processing, compound local binary pattern (CM-LBP) for feature extraction followed by principal component analysis (PCA) for feature reduction. Then, a chaotic whale optimization-based kernel extreme learning machine (CWO-KELM) is utilized to classify the mammograms as normal/abnormal and benign/malignant. The present model achieves the highest accuracy of 100% and 99.48% for MIAS and DDSM, respectively.

关键词： Mammograms compound local binary pattern Chaotic map Whale optimization algorithm Kernel extreme learning machine

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Secure Palmprint Recognition based on Multispectral

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第12期13卷 173-185页

作者： Amraoui, Amine Kerroum, Mounir Ait Fakhri, Youssef Ibn Tofail Univ Fac Sci LaRI Lab Kenitra Morocco Ibn Tofail Univ LaRI Lab ENCG Kenitra Morocco

The security of personal identities is a serious challenge in today's digital world, with so many daily transactions requiring secure solutions. The use of biometric characteristics of the person is presented as the reliable solution to solve this problem. Indeed, this solution is effective, but it hides a weak point which lies in the ability to reproduce certain biometric characteristics for fraud. To overcome this weak point, we propose a secure approach for palmprints that relies on the concept of merging multiple features. Indeed, these features will be extracted from multi-spectral images with different spectra, which allow the extraction of information under the skin of the palm for two different spectrums sequentially in two different times (T1, T2) but instantly. The instant fusion of these characteristics will be impossible to replicate. The images used are grayscale. To satisfy a construction of a reliable and secure system, for this kind of patterns (palmprints), we will use the compound local binary pattern method, since this method adds an additional bit for each P bits coded by LBP corresponding to a neighbor of the local neighborhood, in order to build a robust system. This feature descriptor, it uses both the sign and tilt information of the differences between the central and neighboring gray values. The reliability of the proposed approach has been demonstrated on the Casia Multi-Spectral database. The final experimental results show reliable recognition rates and these recognition rates vary between 99% and 100% for the left and right palms.

关键词： Biometrics multispectral palmprint local features fusion compound local binary pattern

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Multi-instance Finger Knuckle Print Recognition based on Fusion of local Features

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第9期13卷 455-463页

作者： Amaoui, Amine Ait Kerroum, Mounir Fakhri, Youssef Ibn Tofail Univ LaRI Lab Fac Sci Kenitra Morocco Ibn Tofail Univ LaRI Lab ENCG Kenitra Morocco

Personal identity has become an important asset in today's digital world for any individual in society. Biometrics offers itself as a reliable and secure guarantor of our identities, so it has become essential to build efficient and robust recognition systems. In this orientation, we propose a fusion approach, which aims to optimally exploit the dividing block dimensions in the case of local methods to reduce similarities. We will use the compound local binary model (CLBP) for local features extraction, a robust operator descriptor that exploits both the sign and the inclination information of the differences between the center and the neighbor gray values. The reliability of the proposed approach was evaluated on the PolyU Finger Knuckle Print (FKP) database. We presented several experimental results that show the detailed path of our approach, explain the choices made for each step and illustrate the significant improvements compared to other existing recognition systems in the literature. The recognition rate of the proposed global approach is one of the highest among the other methods. Optimal final approach recognition rates vary between 99.70% and 100%.

关键词： Biometrics Finger Knuckle Print local features fusion compound local binary pattern

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Multispectral Palmprint Recognition based on Fusion of local Features 6

Multispectral Palmprint Recognition based on Fusion of Local...

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6th International Conference on Multimedia Computing and Systems (ICMCS)

作者： Amraoui, Amine Fakhri, Youssef Kerroum, Mounir Ait Ibn Tofail Univ Fac Sci LaRIT Lab Kenitra Morocco Ibn Tofail Univ Kenitra ENCG LaRIT Lab Kenitra Morocco

ISBN: (纸本)9781538662205

One of the most serious challenges faced by the present technological world is creating a personal secured identity. Biometrics is presented as an effective solution to resolve these challenges. But this progress is a double-edged knife that allows malicious persons to reproduce biometric modalities. To overcome this problem, we propose a novel approach for palmprint fusing multiple features. These features are provided from multi-spectral images with 940nm, which allows the extraction of information under the skin of the palm. This information is impossible to reproduce. It is noted that in these images, the gray scale information is capital. In this context, compound local binary pattern (CLBP) can be an appropriate approach to construct this system, because the CLBP add an extra bit for each P bits encoded by LBP corresponding to a neighbor of the local neighborhood, in order to construct a robustious feature descriptor that exploits both the sign and the inclination information of the differences between the center and the neighbor gray values. The effectiveness of proposed approach has been verified on Casia Multi-Spectral database. The experimental results show that the recognition rates are reliable and the optimal recognition rates can reach 100% for left and right palms.

关键词： biometrics Multi-spectral palmprint compound local binary pattern local features

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Traffic Sign Recognition Based On Multi-feature Fusion and ELM Classifier 1

Traffic Sign Recognition Based On Multi-feature Fusion and E...

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1st International Conference on Intelligent Computing in Data Sciences (ICDS)

作者： Aziz, Saouli Mohamed, El Aroussi Youssef, Fakhri Univ Ibn Tofail Fac Sci LaRIT Lab BP 242 Kenitra Morocco EHTP SIRC LAGES BP 8108 Casablanca Morocco

This paper proposes a novel and efficient method for traffic sign recognition based on combination of complementary and discriminative feature sets. The extracted features are the histogram of oriented gradients (HOG) feature, Gabor feature and compound local binary pattern (CLBP) feature. The classification is performed using the extreme learning machine (ELM) algorithm. Performances of the proposed approach are evaluated on both German Traffic Sign Recognition Benchmark (GTSRB) and Belgium Traffic Sign Classification (BTSC) Datasets respectively. The results of the experimental work demonstrate that each feature yields fairly high accuracy and the combination of three features has shown good complementariness and yielded fast recognition rate and is more adequate for real-time application as well. (c) 2018 The Authors. Published by Elsevier B.V.

关键词： Traffic sign recognition Histogram of oriented gradients compound local binary pattern Gabor filter Extreme learning machine

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Traffic Sign Recognition Based On Multi-feature Fusion and ELM Classifier

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Procedia Computer Science 2018年 127卷 146-153页

作者： Saouli Aziz El Aroussi Mohamed Fakhri Youssef SIRC/LAGES EHTP BP 8108 Casablanca Morocco

关键词： Traffic sign recognition Histogram of oriented gradients compound local binary pattern Gabor filter Extreme learning machine

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Robust Approaches for Multi-label Face Classification

Robust Approaches for Multi-label Face Classification

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

作者： Mohammed, Ahmed Abdulateef Sajjanhar, Atul Deakin Univ Sch Informat Technol Geelong Vic 3216 Australia

ISBN: (纸本)9781509028962

Texture analysis is widely used to extract facial attributes which are used for face classification. In this paper, novel approaches for texture analysis are proposed to improve the performance of face classification. The proposed methods are derived by applying local binary pattern based approaches on polar raster sampled face images. The proposed methods are evaluated by performing classification of face images using multilabel classification models. Experimental results show that the proposed methods outperform local binary pattern method and its variants.

关键词： texture analysis face classification local binary pattern compound local binary pattern Non-redundant local binary pattern multi-label classification

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