Biometric Authentication Using Finger-Vein Patterns with Deep-Learning and Discriminant Correlation Analysis

2021 ◽  
pp. 2250013
Author(s):  
Aldjia Boucetta ◽  
Leila Boussaad
Keyword(s):  
Correlation Analysis ◽  
Support Vector ◽  
Svm Classifier ◽  
Identification System ◽  
Deep Convolutional Neural Networks ◽  
Finger Vein ◽  
Deep Cnn ◽  
Identification Stage ◽  
Human Finger ◽  
The Right

Finger-vein identification, a biometric technology that uses vein patterns in the human finger to identify people. In recent years, it has received increasing attention due to its tremendous advantages compared to fingerprint characteristics. Moreover, Deep-Convolutional Neural Networks (Deep-CNN) appeared to be highly successful for feature extraction in the finger-vein area, and most of the proposed works focus on new Convolutional Neural Network (CNN) models, which require huge databases for training, a solution that may be more practicable in real world applications, is to reuse pretrained Deep-CNN models. In this paper, a finger-vein identification system is proposed, which uses Squeezenet pretrained Deep-CNN model as feature extractor from the left and the right finger vein patterns. Then, combines this Deep-based features by using a feature-level Discriminant Correlation Analysis (DCA) to reduce feature dimensions and to give the most relevant features. Finally, these composite feature vectors are used as input data for a Support Vector Machine (SVM) classifier, in an identification stage. This method is tested on two widely available finger vein databases, namely SDUMLA-HMT and FV-USM. Experimental results show that the proposed finger vein identification system achieves significant high mean accuracy rates.

scholarly journals Breast cancer detection using deep convolutional neural networks and support vector machines

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6201 ◽  
Author(s):  
Dina A. Ragab ◽  
Maha Sharkas ◽  
Stephen Marshall ◽  
Jinchang Ren
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Input Data ◽  
Data Augmentation ◽  
Area Under The Curve ◽  
Screening Mammography ◽  
Support Vector ◽  
Svm Classifier ◽  
Deep Convolutional Neural Networks ◽  
Cad System

It is important to detect breast cancer as early as possible. In this manuscript, a new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tumors in breast mammography images. In this CAD system, two segmentation approaches are used. The first approach involves determining the region of interest (ROI) manually, while the second approach uses the technique of threshold and region based. The deep convolutional neural network (DCNN) is used for feature extraction. A well-known DCNN architecture named AlexNet is used and is fine-tuned to classify two classes instead of 1,000 classes. The last fully connected (fc) layer is connected to the support vector machine (SVM) classifier to obtain better accuracy. The results are obtained using the following publicly available datasets (1) the digital database for screening mammography (DDSM); and (2) the Curated Breast Imaging Subset of DDSM (CBIS-DDSM). Training on a large number of data gives high accuracy rate. Nevertheless, the biomedical datasets contain a relatively small number of samples due to limited patient volume. Accordingly, data augmentation is a method for increasing the size of the input data by generating new data from the original input data. There are many forms for the data augmentation; the one used here is the rotation. The accuracy of the new-trained DCNN architecture is 71.01% when cropping the ROI manually from the mammogram. The highest area under the curve (AUC) achieved was 0.88 (88%) for the samples obtained from both segmentation techniques. Moreover, when using the samples obtained from the CBIS-DDSM, the accuracy of the DCNN is increased to 73.6%. Consequently, the SVM accuracy becomes 87.2% with an AUC equaling to 0.94 (94%). This is the highest AUC value compared to previous work using the same conditions.

scholarly journals ECG-Based Subject Identification Using Common Spatial Pattern and SVM

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Turky N. Alotaiby ◽  
Saleh A. Alshebeili ◽  
Latifah M. Aljafar ◽  
Waleed M. Alsabhan
Keyword(s):  
Spatial Pattern ◽  
Error Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Identification System ◽  
Equal Error Rate ◽  
Common Spatial Pattern ◽  
Identification Rate ◽  
Subject Identification ◽  
Signal Segment

In this paper, a nonfiducial electrocardiogram (ECG, the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin) identification system based on the common spatial pattern (CSP) feature extraction technique is presented. The single- and multilead ECG signals of each subject are divided into nonoverlapping segments, and different segment lengths (1, 3, 5, 7, 10, or 15 seconds) are investigated. Features are extracted from each signal segment through projection on a CSP projection matrix. The extracted features are then used to train a radial basis function kernel-based Support Vector Machine (SVM) classifier, which is then employed in the identification phase. The proposed identification system was evaluated on 10, 20, …, 200 reference subjects of the Physikalisch-Technische Bundesanstalt (PTB) ECG database. Using a single limb-based lead (I) with 200 reference subjects, the system achieved an identification rate of 95.15% and equal error rate of 0.1. The use of a single chest-based lead (V3) for 200 reference subjects resulted in an identification rate of 98.92% and equal error rate of 0.08.

Secured Finger Vein Authentication System Using Contourlet Transform and SVM

2014 ◽  
Vol 573 ◽  
pp. 465-470
Author(s):  
C. Murukesh ◽  
T.K. Nadesh ◽  
K. Thanushkodi
Keyword(s):  
Recognition Rate ◽  
High Accuracy ◽  
Vital Role ◽  
Security And Privacy ◽  
Contourlet Transform ◽  
Support Vector ◽  
Svm Classifier ◽  
Finger Vein ◽  
Authentication System ◽  
Contemporary Approach

Biometrics authentication is playing a vital role in providing security and privacy. This paper presents a contemporary approach for identifying an individual using unimodal biometrics. Finger vein based authentication system is a promising technology and now-a-days widely used because of its important features such as resistant to criminal tampering, high accuracy, ease of feature extraction and greater authentication speed. The feature vein patterns extracted by Contourlet transform decompose into directional sub bands in different orientations at various scales. The Support Vector Machine (SVM) classifier is used for pattern matching. Thus the experimental results shows that our proposed method tested on two different databases of finger vein images improves recognition rate with high matching speed.

scholarly journals Classification of Pitch and Gender of Speakers for Forensic Speaker Recognition from Disguised Voices Using Novel Features Learned by Deep Convolutional Neural Networks

2021 ◽  
Vol 38 (1) ◽  
pp. 221-230
Author(s):  
Athulya M. Swamidasan Unni Nair ◽  
Sathidevi P. Savithri
Keyword(s):  
Neural Network ◽  
Speaker Recognition ◽  
Three Dimensional ◽  
Support Vector ◽  
Svm Classifier ◽  
High Pitch ◽  
Deep Convolutional Neural Networks ◽  
Mel Frequency Cepstral Coefficients ◽  
Male And Female ◽  
Female Voices

Voice disguise is a major concern in forensic automatic speaker recognition (FASR). Classifying the type of disguise is very important for speaker recognition. Pitch disguise is a very common type of disguise that criminals try to attempt. Among the different types of disguises, high pitch and low pitch voices show more distortion. The features that are robust for high pitch and low pitch voices are different. Moreover, the effect of disguise on male and female voices are also different. In this work, we classified high pitch and low pitch disguised voices for male and female voices using a novel set of features. We arranged Mel frequency cepstral coefficients (MFCC), ΔMFCC, and ΔΔMFCC features as three-dimensional features, and these are given as the RGB equivalent spectrogram inputs to pretrained AlexNet deep convolutional neural network (DCNN). We fused the AlexNet output features with corresponding MFCC correlation features. These fused features are the proposed novel features for disguise classification. Classification using neural network (NN) and support vector machine (SVM) classifiers are performed. Simulation results show that classification with SVM classifier using these novel features gives improved accuracy of 98.89% compared to 95.99% accuracy obtained by using DCNN output features using traditional spectrogram inputs.

scholarly journals Cursor Movement Detection in Brain-Computer-Interface Systems Using the Hybrid K-Means Clustering Method and LSVM

2020 ◽  
Author(s):  
Leila Mohammadi ◽  
zahra einalou ◽  
Hamidreza Hosseinzadeh ◽  
Mehrdad Dadgostar
Keyword(s):  
Feature Extraction ◽  
Clustering Algorithm ◽  
Feature Vector ◽  
Support Vector ◽  
Svm Classifier ◽  
Clustering Method ◽  
Feature Extraction And Selection ◽  
New Feature ◽  
Linear Svm ◽  
The Right

Abstract In this study, we present the detection of the up- and downward as well as the right- and leftward motion of cursor based on feature extraction. Feature Extraction and selection for finding the proper classifier among the data mining methods are of great importance. In the proposed method, the hybrid K-means clustering algorithm and the linear support vector machine (LSVM) classifier have been used for extracting the important features and detecting the cursor motion. In this algorithm, the K-means clustering method is used to recognize the available hidden patterns in each of the four modes (up, down, left, and right). The identification of these patterns can raise the accuracy of classification. The membership degree of each feature vector in the proposed new patterns is considered as a new feature vector corresponding to the previous feature vector and then, the cursor motion is detected using the linear SVM classifier. The database of the Karadeniz Technical University of Turkey has been used in the present article. Applying the proposed method for data based on the hold-up cross validation causes the accuracy of the classifier in the up- and downward and left- and rightward movements in each person to increase by 2–10%.

A Computer Vision-Based Method for Classification of Red Meat Quality After Nitrosamine Appendage

2021 ◽  
pp. 2150005
Author(s):  
Monika Arora ◽  
Parthasarathi Mangipudi
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Food Samples ◽  
Red Meat ◽  
Support Vector ◽  
Svm Classifier ◽  
Deep Convolutional Neural Networks ◽  
Improved Performance ◽  
Artificial Neural Network Ann ◽  
Ann Classifier

Nitrosamine is a carcinogenic chemical used as a preservative in red meat whose identification is an ordeal. This paper presents a computer vision-based non-destructive method for identifying quality disparities between preservative treated and untreated (control) red meat. To access the discrepancy in the quality of red meat, both traditional machine learning and deep learning-based methods have been used. Support vector machine (SVM) classifier and artificial neural network (ANN) models have been used to detect the presence of nitrosamine in test samples. The paper also made use of different pre-trained deep convolutional neural networks (DCNN) with transfer learning approach such as ResNet-34, ResNet-50, ResNet-101, VGG-16, VGG-19, AlexNet and MobileNetv2 to examine the presence of nitrosamine in the food samples. While the ANN classifier performed better in comparison to the SVM classifier, the highest testing accuracy and F1-score were obtained using the deep learning model, ResNet-101 with 95.45% and 96.54%, respectively. The experimental results demonstrate an improved performance in comparison to the existing methods; indicating the feasibility of the proposed work for food quality control in real-time applications.

Centre-of-mass-based gait recognition for person identification

2019 ◽  
Vol 13 ◽  
Author(s):  
Rajib Ghosh
Keyword(s):  
Body Movement ◽  
Gait Recognition ◽  
Visual Surveillance ◽  
Support Vector ◽  
Svm Classifier ◽  
Identification System ◽  
Centre Of Mass ◽  
Person Identification ◽  
Walking Pattern ◽  
Walking Activity

Background: Gait recognition focuses on identification of persons from their walking activity. This type of system plays an important role in visual surveillance applications. Walking pattern of every person is unique and difficult to replicate by others. Objective: The present article focuses on to develop a person identification system based on gait recognition. Methods: In this article, a novel gait recognition approach is proposed to show how human body Centre-of-mass-based walking characteristics can be used to recognize unauthorized and suspicious persons when they enter in a surveillance area. Walking pattern varies from person to person mainly due to the differences in the footsteps and body movement. Initially, background is modelled from the input video captured through static cameras deployed for security purpose. Foreground moving object in the individual frames are then segmented using the background subtraction algorithm. Centre-of-mass based discriminative features of various walking patterns are then studied using Support Vector Machine(SVM) classifier to identify each unique walking pattern. Results: The proposed system has been evaluated using a self-generated dataset containing side view of various walking video clips. The experimental results demonstrate that the proposed system achieves an encouraging person identification rate. Conclusion: This work can be further extended to provide a general approach in developing an automatic person identification system in unconstrained environment.

scholarly journals Influence of Pre-Processing Strategies on the Performance of ML Classifiers Exploiting TF-IDF and BOW Features

2020 ◽  
Vol 9 (2) ◽  
pp. 49-68
Author(s):  
Amit Purushottam Pimpalkar ◽  
R. Jeberson Retna Raj
Keyword(s):  
Positive Impact ◽  
Support Vector ◽  
Svm Classifier ◽  
Features Selection ◽  
Data Set ◽  
Processing Strategies ◽  
Document Frequency ◽  
Accuracy And Precision ◽  
The Right ◽  
Processing Techniques

Data analytics and its associated applications have recently become impor-tant fields of study. The subject of concern for researchers now-a-days is a massive amount of data produced every minute and second as people con-stantly sharing thoughts, opinions about things that are associated with them. Social media info, however, is still unstructured, disseminated and hard to handle and need to be developed a strong foundation so that they can be utilized as valuable information on a particular topic. Processing such unstructured data in this area in terms of noise, co-relevance, emoticons, folksonomies and slangs is really quite challenging and therefore requires proper data pre-processing before getting the right sentiments. The dataset is extracted from Kaggle and Twitter, pre-processing performed using NLTK and Scikit-learn and features selection and extraction is done for Bag of Words (BOW), Term Frequency (TF) and Inverse Document Frequency (IDF) scheme. For polarity identification, we evaluated five different Machine Learning (ML) algorithms viz Multinomial Naive Bayes (MNB), Logistic Regression (LR), Decision Trees (DT), XGBoost (XGB) and Support Vector Machines (SVM). We have performed a comparative analysis of the success for these algorithms in order to decide which algorithm works best for the given data-set in terms of recall, accuracy, F1-score and precision. We assess the effects of various pre-processing techniques on two datasets; one with domain and other not. It is demonstrated that SVM classifier outperformed the other classifiers with superior evaluations of 73.12% and 94.91% for accuracy and precision respectively. It is also highlighted in this research that the selection and representation of features along with various pre-processing techniques have a positive impact on the performance of the classification.  The ultimate outcome indicates an improvement in sentiment classification and we noted that pre-processing approaches obviously suggest an improvement in the efficiency of the classifiers.

scholarly journals Fruit Disease Detection Using GLCM And SVM Classifier

2019 ◽  
pp. 365-371
Author(s):  
Anu S ◽  
Nisha T ◽  
Ramya R ◽  
Rizuvana Farvin M
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Decision Tree ◽  
Critical Role ◽  
Support Vector ◽  
Svm Classifier ◽  
Gray Level ◽  
Fruit Disease ◽  
Occurrence Matrix ◽  
The Right

Analytics plays a critical role in detecting and analyzing the diseases. The proposed system identifies the fruits that are affected with diseases. It is done by collecting the raw data which is subjected to pre-processing. It results in a HSV (hue, saturation, value) converted image. After pre-processing, the resized format of the data is used to extract the information. In feature extraction the image is segmented and converted into matrix using Gray level co-occurrence matrix algorithm. The further classification is done and result is represented in the form of a decision tree using Support Vector Machine (SVM). The disease that affected the fruit is displayed along with the right fertilizer to be used for the plant.

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

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