scholarly journals A novel hybrid face recognition framework based on a low-resolution camera for biometric applications

2021 ◽  
Vol 24 (2) ◽  
pp. 853
Author(s):  
Vijaya Kumar H. R. ◽  
M. Mathivanan
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Research Work ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Low Resolution ◽  
Human Face Recognition ◽  
Web Camera ◽  
Multiclass Support Vector Machine ◽  
Human Faces

In research work, human face recognition is an essential biometric symbol persistently continued so far due to its different levels of applications in society. Since the appearance of the human faces can have many variations due to issues like the effect of illumination, expression and face pose. These differences are correlated with one another, which results in a helpless ability to recognize a particular person's face. The motivation behind our work in this paper is to give a new framework for face recognition based on frequency analysis that contributes to solving the distinguishing proof issues with enormous varieties of boundaries like the effect of illumination, expression, and face pose. Here three algorithms combined for provable results: i) Difference of Gaussian filtered discrete wavelet transform (DDWT) for feature extraction; ii) Log Gabor (LG) filter for feature extraction; and iv) Multiclass support vector machine classifier, where feature coefficients of DDWT and LG filter are fused for classification and parameters evaluation. The evaluation of our experiment is carried out on a large database consisting of 15 persons of each 200-face image which are captured using a 5-megapixel low-resolution web camera and yielding satisfactory results on various parameters compared to existing methods.

Effects of the Window Size and Feature Extraction Approach for Arrhythmia Classification

2017 ◽  
Vol 30 ◽  
pp. 1-11 ◽  
Author(s):  
Khudhur A. Alfarhan ◽  
Mohd Yusoff Mashor ◽  
Abdul Rahman Mohd Saad ◽  
Hayder A. Azeez ◽  
Mustafa M. Sabry
Keyword(s):  
Feature Extraction ◽  
Autoregressive Model ◽  
Research Work ◽  
Window Size ◽  
Extraction Methods ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Ecg Signals ◽  
Library Support ◽  
Electrocardiogram Ecg

Arrhythmia, a common form of heart disease, can be detected from an electrocardiogram (ECG) signal. This research work presents a comparative study between five feature extraction methods applied separately on two window sizes for detecting three ECG pulse types, namely normal and two arrhythmia variations. The library support vector machine (LIBSVM) was used to classify the three classes of the ECG pulses. The ECG signals were obtained from MIT-BIH database. The ECG dataset was normalized and filtered to remove any noise and after that the signals were windowed into two window sizes (long window and short window). Five approaches were used to extract the features from the ECG signals. These approaches are scalar Autoregressive model coefficients, Haar discrete wavelet transform (DWT), Daubechies (db) DWT, Biorthogonal (bior) DWT, and principal components analysis (PCA). Each approach was applied separately on the two window sizes. The results of the classification show that scalar Autoregressive model coefficients, Haar, db, and bior are better approaches to catch the ECG features for short window than the long window. However, PCA gave the closest and highest results for the two window sizes than other approaches. That mean the PCA is the better feature extraction approach for both window sizes.

Real-time Mental Stress Detection Technique Using Neural Networks towards Wearable Health Monitor

2021 ◽  
Author(s):  
Nilava Mukherjee ◽  
Sumitra Mukhopadhyay ◽  
Rajarshi Gupta
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Mental Stress ◽  
Detection Technique ◽  
Physiological Signals ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Optimum Number ◽  
Stress Detection ◽  
Detection Techniques

Abstract Motivation: In recent times, mental stress detection using physiological signals have received widespread attention from the technology research community. Although many motivating research works have already been reported in this area, the evidence of hardware implementation is occasional. The main challenge in stress detection research is using optimum number of physiological signals, and real-time detection with low complexity algorithm. Objective: In this work, a real-time stress detection technique is presented which utilises only photoplethysmogram (PPG) signal to achieve improved accuracy over multi-signal-based mental stress detection techniques. Methodology: A short segment of 5s PPG signal was used for feature extraction using an autoencoder (AE), and features were minimized using recursive feature elimination (RFE) integrated with a multi-class support vector machine (SVM) classifier. Results: The proposed AE-RFE-SVM based mental stress detection technique was tested with WeSAD dataset to detect four-levels of mental state, viz., baseline, amusement, meditation and stress and to achieve an overall accuracy, F1 score and sensitivity of 99%, 0.99 and 98% respectively for 5s PPG data. The technique provided improved performance over discrete wavelet transformation (DWT) based feature extraction followed by classification with either of the five types of classifiers, viz., SVM, random forest (RF), k-nearest neighbour (k-NN), linear regression (LR) and decision tree (DT). The technique was translated into a quad-core-based standalone hardware (1.2 GHz, and 1 GB RAM). The resultant hardware prototype achieves a low latency (~0.4 s) and low memory requirement (~1.7 MB). Conclusion: The present technique can be extended to develop remote healthcare system using wearable sensors.

Automated Ripeness Assessment System of Tomatoes Using PCA and SVM Techniques

2014 ◽  
pp. 101-130 ◽  
Author(s):  
Esraa El Hariri ◽  
Nashwa El-Bendary ◽  
Aboul Ella Hassanien ◽  
Amr Badr
Keyword(s):  
Feature Extraction ◽  
Fruits And Vegetables ◽  
Research Work ◽  
Assessment System ◽  
Assessment Process ◽  
Support Vector ◽  
Work Related ◽  
Color Features ◽  
Core Concepts

One of the prime factors in ensuring a consistent marketing of crops is product quality, and the process of determining ripeness stages is a very important issue in the industry of (fruits and vegetables) production, since ripeness is the main quality indicator from the customers' perspective. To ensure optimum yield of high quality products, an objective and accurate ripeness assessment of agricultural crops is important. This chapter discusses the problem of determining different ripeness stages of tomato and presents a content-based image classification approach to automate the ripeness assessment process of tomato via examining and classifying the different ripeness stages as a solution for this problem. It introduces a survey about resent research work related to monitoring and classification of maturity stages for fruits/vegetables and provides the core concepts of color features, SVM, and PCA algorithms. Then it describes the proposed approach for solving the problem of determining different ripeness stages of tomatoes. The proposed approach consists of three phases, namely pre-processing, feature extraction, and classification phase. The classification process depends totally on color features (colored histogram and color moments), since the surface color of a tomato is the most important characteristic to observe ripeness. This approach uses Principal Components Analysis (PCA) and Support Vector Machine (SVM) algorithms for feature extraction and classification, respectively.

Low resolution face recognition based on support vector data description

2006 ◽  
Vol 39 (9) ◽  
pp. 1809-1812 ◽  
Author(s):  
Sang-Woong Lee ◽  
Jooyoung Park ◽  
Seong-Whan Lee
Keyword(s):  
Face Recognition ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Low Resolution ◽  
Data Description

3-D FACE MODELING AND ITS APPLICATIONS

1996 ◽  
Vol 10 (05) ◽  
pp. 491-520 ◽  
Author(s):  
THOMAS S. HUANG ◽  
LI-AN TANG
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Computer Animation ◽  
Video Sequence ◽  
Human Face ◽  
Face Modeling ◽  
Human Face Recognition ◽  
Preliminary Results ◽  
Human Faces ◽  
Sequence Compression

This paper describes some issues in building a 3-D human face modeling system which mainly consists of three parts: • Modeling human faces; • Analyzing facial motions; • Synthesizing facial expressions. A variety of techniques developed for this system are described in detail in this paper. Some preliminary results of applying this system to computer animation, video sequence compression and human face recognition are also shown.

scholarly journals Wavelet Statistical Feature based Malware Class Recognition and Classification using Supervised Learning Classifier

2017 ◽  
Vol 10 (2) ◽  
pp. 400-406 ◽  
Author(s):  
Aziz Makandar ◽  
Anita Patrot
Keyword(s):  
Feature Extraction ◽  
Automated Analysis ◽  
Input Image ◽  
Access Time ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Learning Classifier ◽  
Feature Based ◽  
Three Stages ◽  
Analysis System

Malware is a malicious instructions which may harm to the unauthorized private access through internet. The types of malware are incresing day to day life, it is a challenging task for the antivius vendors to predict and caught on access time. This paper aims to design an automated analysis system for malware classes based on the features extracted by Discrete Wavelet Transformation (DWT) and then by applying four level decomposition of malware. The proposed system works in three stages, pre-processing, feature extraction and classification. In preprocessing, input image is normalized in to 256x256 by applying wavelet we are denoising the image which helps to enhance the image. In feature extraction, DWT is used to decompose image into four level. For classification the support vector machine (SVM) classifiers are used to discriminate the malware classes with statistical features extracted from level 4 decomposition of DWT such as Daubechies (db4), Coiflet (coif5) and Bi-orthogonal (bior 2.8). Among these wavelet features the db4 features effectively classify the malware class type with high accuracy 91.05% and 92.53% respectively on both dataset. The analysis of proposed method conducted on two dataset and the results are promising.

scholarly journals Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2403
Author(s):  
Jakub Browarczyk ◽  
Adam Kurowski ◽  
Bozena Kostek
Keyword(s):  
Feature Extraction ◽  
Principal Component ◽  
Component Analysis ◽  
Mental States ◽  
Extraction Methods ◽  
Support Vector ◽  
Discrete Wavelet ◽  
K Nearest Neighbors ◽  
Vector Machines

The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was performed on obtained signals. Welch’s method, autoregressive modeling, and discrete wavelet transform were used for feature extraction. Principal component analysis (PCA) was performed in order to reduce the dimensionality of feature vectors. k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Neural Networks (NN) were employed for classification. Precision, recall, F1 score, as well as a discussion based on statistical analysis, were shown. The paper also contains code utilized in preprocessing and the main part of experiments.

scholarly journals General model for best feature extraction of EEG using discrete wavelet transform wavelet family and differential evolution

2020 ◽  
Vol 16 (3) ◽  
pp. 155014772091100 ◽  
Author(s):  
Ahmad al-Qerem ◽  
Faten Kharbat ◽  
Shadi Nashwan ◽  
Staish Ashraf ◽  
khairi blaou
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Differential Evolution ◽  
Discrete Wavelet Transform ◽  
Research Work ◽  
Discrete Wavelet ◽  
Proposed Model ◽  
Feature Extraction And Selection ◽  
Selection Step ◽  
Wavelet Family

Wavelet family and differential evolution are proposed for categorization of epilepsy cases based on electroencephalogram (EEG) signals. Discrete wavelet transform is widely used in feature extraction step because it efficiently works in this field, as confirmed by the results of previous studies. The feature selection step is used to minimize dimensionality by excluding irrelevant features. This step is conducted using differential evolution. This article presents an efficient model for EEG classification by considering feature extraction and selection. Seven different types of common wavelets were tested in our research work. These are Discrete Meyer (dmey), Reverse biorthogonal (rbio), Biorthogonal (bior), Daubechies (db), Symlets (sym), Coiflets (coif), and Haar (Haar). Several kinds of discrete wavelet transform are used to produce a wide variety of features. Afterwards, we use differential evolution to choose appropriate features that will achieve the best performance of signal classification. For classification step, we have used Bonn databases to build the classifiers and test their performance. The results prove the effectiveness of the proposed model.

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