Robust multimodal biometric authentication algorithms using fingerprint, iris and voice features fusion

2021 ◽  
Vol 40 (1) ◽  
pp. 647-672
Author(s):  
Mohamed S. El_Tokhy
Keyword(s):  
Feature Fusion ◽  
Boundary Energy ◽  
Roc Curves ◽  
Point Of View ◽  
Support Vector ◽  
Svm Classifier ◽  
Biometric Authentication ◽  
Classification Rate ◽  
Computational Point ◽  
Practical Applications

Development of a robust triple multimodal biometric approach for human authentication using fingerprint, iris and voice biometric is the main objective of this manuscript. Accordingly, three essential algorithms for biometric authentication are presented. The extracted features from these multimodals are combined via feature fusion center (FFC) and feature scores. These features are trained through artificial neural network (ANN) and support vector machine (SVM) classifiers. The first algorithm depends on boundary energy method (BEM) extracted features from fingerprint, normalized combinational features from iris and dimensionality reduction methods (DRM) from voice using sum/average FFC. The second proposed algorithm uses extracted features from zoning method of fingerprint, SIFT of iris and higher order statistics (HOS) of voice signals. The third proposed algorithm consists of extracted features from zoning method for fingerprint, SIFT from iris and DRM from voice signals. Classification accuracy of implemented algorithms is estimated. Comparison between proposed algorithms is introduced in terms of equal error rate (EER) and ROC curves. The experimental results confirm superiority of second proposed algorithm which achieves a classification rate of 100% using SVM classifier and sum FFC. From computational point of view, the first algorithm consumes the lowest time using SVM classifier. On other hand, the lowest EER is achieved by first proposed algorithm for extracted features from Karhunen-Loeve transform (KLT) method of DRM. Additionally, the lowest ROC curves are accomplished respectively for extracted features from multidimensional scaling (MDS), generated ARMA synthesis and Isomap features. Their accuracy is improved with SVM. Also, the sum FFC introduces efficient results compared to average FFC. These algorithms have the advantages of robustness and the strength of selecting unimodal, double and triple biometric authentication. The obtained results accomplish a remarkable accuracy for authentication and security within multi practical applications.

An Enhanced Facial Expression Recognition Model Using Local Feature Fusion of Gabor Wavelets and Local Directionality Patterns

2020 ◽  
Vol 11 (1) ◽  
pp. 48-70 ◽  
Author(s):  
Sivaiah Bellamkonda ◽  
Gopalan N.P
Keyword(s):  
Facial Expression ◽  
Feature Fusion ◽  
Support Vector ◽  
Svm Classifier ◽  
Source Image ◽  
Expression Recognition ◽  
Fusion Model ◽  
Average Accuracy ◽  
Benchmark Datasets ◽  
Operator Fatigue

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.

A Novel Classification Method of Fish Based on Multi-Feature Fusion

2015 ◽  
Vol 713-715 ◽  
pp. 1513-1519 ◽  
Author(s):  
Wei Dong Du ◽  
Bao Wei Chen ◽  
Hai Sen Li ◽  
Chao Xu
Keyword(s):  
Discrete Cosine Transform ◽  
Feature Fusion ◽  
Acoustic Scattering ◽  
Scattering Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Correct Identification ◽  
Classification Problems ◽  
Cosine Transform ◽  
Identification Rate

In order to solve fish classification problems based on acoustic scattering data, temporal centroid (TC) features and discrete cosine transform (DCT) coefficients features used to analyze acoustic scattering characteristics of fish from different aspects are extracted. The extracted features of fish are reduced in dimension and fused, and support vector machine (SVM) classifier is used to classify and identify the fishes. Three kinds of different fishes are selected as research objects in this paper, the correct identification rates are given based on temporal centroid features and discrete cosine transform coefficients features and fused features. The processing results of actual experimental data show that multi-feature fusion method can improve the identification rate at about 5% effectively.

scholarly journals Fingerprint Liveness Detection Based on Fine-Grained Feature Fusion for Intelligent Devices

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 517
Author(s):  
Xinting Li ◽  
Weijin Cheng ◽  
Chengsheng Yuan ◽  
Wei Gu ◽  
Baochen Yang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Feature Fusion ◽  
Texture Feature ◽  
Fusion Rule ◽  
Support Vector ◽  
Svm Classifier ◽  
Fine Grained ◽  
Liveness Detection ◽  
Fingerprint Liveness Detection ◽  
Intelligent Devices

Currently, intelligent devices with fingerprint identification are widely deployed in our daily life. However, they are vulnerable to attack by fake fingerprints made of special materials. To elevate the security of these intelligent devices, many fingerprint liveness detection (FLD) algorithms have been explored. In this paper, we propose a novel detection structure to discriminate genuine or fake fingerprints. First, to describe the subtle differences between them and take advantage of texture descriptors, three types of different fine-grained texture feature extraction algorithms are used. Next, we develop a feature fusion rule, including five operations, to better integrate the above features. Finally, those fused features are fed into a support vector machine (SVM) classifier for subsequent classification. Data analysis on three standard fingerprint datasets indicates that the performance of our method outperforms other FLD methods proposed in recent literature. Moreover, data analysis results of blind materials are also reported.

The recognition of plastic bottle using linear multi hierarchical SVM classifier

2021 ◽  
pp. 1-14
Author(s):  
LiHua Cai ◽  
Jin Cao ◽  
MingQiang Wang ◽  
Ta Zhou ◽  
HaiFeng Fang
Keyword(s):  
Loss Function ◽  
Deep Structure ◽  
Principal Component ◽  
Classification Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Input Matrix ◽  
Classification Rate ◽  
Pet Bottles ◽  
Combination Methods

Both classification rate and accuracy are crucial for the recyclable PET bottles, and the existing combination methods of SVM all simply use SVM as the unit classifier, ignoring the improvement of SVM’s classification performance in the training process of deep learning. A linear multi hierarchical deep structure based on Support Vector Machine (SVM) is proposed to cover this problem. A novel definition of the input matrix in each layer enhances the optimization of Lagrange multipliers in Sequential Minimal Optimization (SMO) algorithm, thus the datapoint in maximum interval of SVM hyperplane could be recognized, improving the classification performance of SVM classifier in this layer. The loss function defined in this paper could control the depth of Linear Multi Hierarchical SVM (LMHSVM), the generalization parameters are added in the loss function and the input matrix to enhance the generalization performance of LMHSVM. The process of creating Bottle dataset by Histogram of Oriented Gradient (HOG) and Principal Component Analysis (PCA) is introduced meanwhile, reducing the data size of bottles. Experiments are conducted on LMHSVM and multiple typical classification algorithms with Bottle dataset and UCI datasets, the results indicated that LMHSVM has excellent classification performances than FNN classifier, LIBSVM (Gaussian) and GFS-AdaBoost-C in KEEL.

scholarly journals Classification of Arabic fricative consonants according to their places of articulation

2022 ◽  
Vol 12 (1) ◽  
pp. 936
Author(s):  
Youssef Elfahm ◽  
Nesrine Abajaddi ◽  
Badia Mounir ◽  
Laila Elmaazouzi ◽  
Ilham Mounir ◽  
...  
Keyword(s):  
Energy Distribution ◽  
Voice Recognition ◽  
Point Of View ◽  
Support Vector ◽  
Acoustic Cues ◽  
Classification Rate ◽  
Practical Point ◽  
Vector Machines ◽  
Speech Identification

<span>Many technology systems have used voice recognition applications to transcribe a speaker’s speech into text that can be used by these systems. One of the most complex tasks in speech identification is to know, which acoustic cues will be used to classify sounds. This study presents an approach for characterizing Arabic fricative consonants in two groups (sibilant and non-sibilant). From an acoustic point of view, our approach is based on the analysis of the energy distribution, in frequency bands, in a syllable of the consonant-vowel type. From a practical point of view, our technique has been implemented, in the MATLAB software, and tested on a corpus built in our laboratory. The results obtained show that the percentage energy distribution in a speech signal is a very powerful parameter in the classification of Arabic fricatives. We obtained an accuracy of 92% for non-sibilant consonants /f, χ, ɣ, ʕ, ћ, and h/, 84% for sibilants /s, sҁ, z, Ӡ and ∫/, and 89% for the whole classification rate. In comparison to other algorithms based on neural networks and support vector machines (SVM), our classification system was able to provide a higher classification rate.</span>

Classification of Hemodynamics Using a Diagnostic Nomogram and Ambulatory Blood Pressure Data

2020 ◽  
Vol 23 (3) ◽  
pp. 291-298
Author(s):  
M.V. Voitikova ◽  
R.V. Khursa
Keyword(s):  
Blood Pressure ◽  
Ambulatory Blood Pressure ◽  
Blood Pressure Monitoring ◽  
Support Vector ◽  
Svm Classifier ◽  
Practical Applications ◽  
Nonlinear Properties ◽  
Blood Pressure Data ◽  
Linear Regression Modeling

This article provides a detailed overview of the hemodynamic nomogram, a new diagnostic tool in hemodynamics based on the linear regression modeling of ambulatory blood pressure monitoring data (ABPM) and the Support Vector Machine (SVM) classifier. We investigated the classification capability and practical usage of the diagnostic nomogram, as well as cardiovascular phenomena described and limitations of linear modeling.One of the practical applications of the nomogram is the ability to retrace changes in ABPM parameters due to antihypertensive therapy. Misclassifications are explained by nonlinear properties of the hemodynamics of diastolic type.

scholarly journals Healthy Fruits Image Label Categorization through Color Shape and Texture Features Based on Machine Learning Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 34-40
Keyword(s):  
Machine Learning ◽  
Feature Fusion ◽  
Learning Algorithm ◽  
Texture Features ◽  
Classification Model ◽  
Support Vector ◽  
Svm Classifier ◽  
Paper Machine ◽  
Textual Features ◽  
Occurrence Matrix

The fruit categorization according to their visual quality has recently experienced tremendous growth in the field of agriculture and food products. Due to post-harvest loses during handling and processing, there is an increasing demand for quality products in agro industry which requires accuracy to predict the fruit. Various techniques of machine learning have been successfully applied for classifying the fruit built on binary class. In this paper, machine leaning technique is used to automate the process of categorization and to improve the accuracy of different types of fruits by feature selection. To categorized images domain specific features such as color, shape and textual features are considered. Statistical color features are extracted from the image, bounding box feature for shape features and gray-level co-occurrence matrix (GLCM) is used to extract the textual feature of an image. These features are combined in a single feature fusion. A support vector machine (SVM) classification model is trained using training set features on fruit360 dataset which includes six fruit categories (classes) with two sub category (sub-classes) which builds multiclass classification task. We present one-vs-one coding design of Error correcting output codes (ECOC) and apply to SVM classifier; validation followed a fivefold cross validation strategy. The result shows that the textual features combined with color and shape feature improved fruit classification accuracy.

scholarly journals Visualization of numerical results obtained for gas-particle flows using Lagrangian approaches to the dispersed phase description

2018 ◽  
pp. 522-539
Author(s):  
И.В. Тетерина ◽  
В.Н. Емельянов ◽  
К.Н. Волков
Keyword(s):  
Point Of View ◽  
Two Phase ◽  
Computational Point ◽  
Practical Applications ◽  
Particle Flows ◽  
Representation Of Solutions ◽  
Gas Dynamics Problems ◽  
Control Volumes ◽  
Traditional Approaches ◽  
Dynamics Problems

Рассматриваются вопросы, связанные с визуализацией течений, содержащих твердые частицы или жидкие капли, в различных практических приложениях. Приводятся примеры визуального представления решений ряда задач двухфазной газовой динамики, связанных с расчетами течений в каналах и вихревых структурах и полученных при помощи лагранжевых подходов. Помимо традиционных подходов к визуализации вихревых течений с частицами и каплями, основанных на построении линий уровня различных характеристик потока, фазовых траекторий и распределений концентрации дискретных включений, применяются сечения Пуанкаре и метод локальных показателей Ляпунова, а также различные критерии идентификации вихревых образований в поле течения. Обсуждается дисперсия частиц в турбулентном потоке и формирование областей с повышенным содержанием дисперсной фазы. В логическом отношении лагранжевый подход к описанию двухфазных течений является простым, но в вычислительном отношении достаточно трудоемким, поскольку для имитации движения примеси требуется проведение большого числа траекторных расчетов пробных частиц. Дополнительные вычислительные трудности связаны с необходимостью локализации частиц в контрольных объемах неструктурированной сетки и восполнением параметров несущего потока. Some issues related to the implementation and physical and mathematical support of computational experiments on the investigation of fluid and gas flows containing Lagrangian coherent vortex structures are considered. Methods and tools designed to visualize vortical flows arising in various practical applications are discussed. Examples of visual representation of solutions of gas dynamics problems computed with Lagrangian approaches to the description of flows of fluid and gas are provided. In addition to traditional approaches to the visualization of vortex flows based on the construction of contours of various flow quantities, the phase trajectories of Lagrangian particles, the Poincare section, and the local Lyapunov exponent method are applied. The Lagrangian approach to the description of two-phase flows is relatively simple, but time-consuming from the computational point of view, because it requires a large number of trajectory calculations of sample particles. Additional computational difficulties come from the need of localization of particles in the control volumes of unstructured mesh and interpolation of flow quantities of gas phase.

scholarly journals A Hybrid Feature Selection and Extraction Methods for Sleep Apnea Detection Using Bio-Signals

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4323 ◽  
Author(s):  
Xilin Li ◽  
Sai Ho Ling ◽  
Steven Su
Keyword(s):  
Feature Selection ◽  
Sleep Apnea ◽  
Regularization Parameter ◽  
Roc Curves ◽  
Extraction Methods ◽  
Classification Performance ◽  
Hill Climbing ◽  
Support Vector ◽  
Svm Classifier ◽  
Increased Risk

People with sleep apnea (SA) are at increased risk of having stroke and cardiovascular diseases. Polysomnography (PSG) is used to detect SA. This paper conducts feature selection from PSG signals and uses a support vector machine (SVM) to detect SA. To analyze SA, the Physionet Apnea Database was used to obtain various features. Electrocardiography (ECG), oxygen saturation (SaO2), airflow, abdominal, and thoracic signals were used to provide various frequency-, time-domain and non-linear features (n = 87). To analyse the significance of these features, firstly, two evaluation measures, the rank-sum method and the analysis of variance (ANOVA) were used to evaluate the significance of the features. These features were then classified according to their significance. Finally, different class feature sets were presented as inputs for an SVM classifier to detect the onset of SA. The hill-climbing feature selection algorithm and the k-fold cross-validation method were applied to evaluate each classification performance. Through the experiments, we discovered that the best feature set (including the top-five significant features) obtained the best classification performance. Furthermore, we plotted receiver operating characteristic (ROC) curves to examine the performance of the SVM, and the results showed the SVM with Linear kernel (regularization parameter = 1) outperformed other classifiers (area under curve = 95.23%, sensitivity = 94.29%, specificity = 96.17%). The results confirm that feature subsets based on multiple bio-signals have the potential to identify patients with SA. The use of a smaller subset avoids dimensionality problems and reduces the computational load.

Automated Municipal Solid Waste Sorting for Recycling Using a Mobile Manipulator

2016 ◽  
Author(s):  
Sathish Gundupalli Paulraj ◽  
Subrata Hait ◽  
Atul Thakur
Keyword(s):  
Municipal Solid Waste ◽  
Solid Waste ◽  
Mobile Manipulator ◽  
Support Vector ◽  
Svm Classifier ◽  
Automated Identification ◽  
Classification Rate ◽  
Rapid Urbanization ◽  
Clustering Model ◽  
Thermographic Image

Municipal solid waste (MSW), generated at an unprecedented rate due to rapid urbanization and industrialization contains useful recyclable materials like metals, plastic, wood, etc. Recycling of useful materials from MSW in the developing countries is severely constrained by limited door-to-door collection and poor means of segregation. Recovery of recyclables is usually performed by waste pickers, which is highly risky and hazardous for their health. This paper reports the development of a robotic mobile manipulation system for automated sorting of useful recyclables from MSW. The developed robot is equipped with a thermal imaging camera, proximity sensor and a 5-DOF robotic arm. This paper presents an approach for sorting based on automated identification from thermographic images. The developed algorithm extracts keypoint features from the thermographic image and feeds into clustering model to map them into a bag-of-word vectors. Finally, Support Vector Machine (SVM) classifier is used for identifying the recyclable material. We used the developed algorithm to detect three categories of recyclables namely, aluminum can, plastic bottle and tetra pack from given thermographic images. We obtained classification rate of 94.3% in the tests. In future, we plan to extend the developed approach for classifying a wider range of recyclable objects as well as to incorporate motion planning algorithms to handle cluttered environments.

Sign in / Sign up

Export Citation Format

Share Document