scholarly journals ALGORITHMS FOR FINGERPRINT CLASSIFICATION

2021 ◽  
Vol 335 (1) ◽  
pp. 39-44
Author(s):  
D. Lebedev ◽  
A. Abzhalilova
Keyword(s):  
Neural Network ◽  
Gabor Filter ◽  
Recognition System ◽  
Personal Identification ◽  
External Object ◽  
Biometric Identification ◽  
Fingerprint Classification ◽  
Combined Application ◽  
Traditional Approaches

Currently, biometric methods of personality are becoming more and more relevant recognition technology. The advantage of biometric identification systems, in comparison with traditional approaches, lies in the fact that not an external object belonging to a person is identified, but the person himself. The most widespread technology of personal identification by fingerprints, which is based on the uniqueness for each person of the pattern of papillary patterns. In recent years, many algorithms and models have appeared to improve the accuracy of the recognition system. The modern algorithms (methods) for the classification of fingerprints are analyzed. Algorithms for the classification of fingerprint images by the types of fingerprints based on the Gabor filter, wavelet - Haar, Daubechies transforms and multilayer neural network are proposed. Numerical and results of the proposed experiments of algorithms are carried out. It is shown that the use of an algorithm based on the combined application of the Gabor filter, a five-level wavelet-Daubechies transform and a multilayer neural network makes it possible to effectively classify fingerprints.

ALGORITHMS FOR FINGERPRINT CLASSIFICATION

2021 ◽  
Vol 335 (1) ◽  
pp. 39-44
Author(s):  
D. Lebedev ◽  
A. Abzhalilova
Keyword(s):  
Neural Network ◽  
Gabor Filter ◽  
Recognition System ◽  
Personal Identification ◽  
External Object ◽  
Biometric Identification ◽  
Fingerprint Classification ◽  
Combined Application ◽  
Traditional Approaches

Currently, biometric methods of personality are becoming more and more relevant recognition technology. The advantage of biometric identification systems, in comparison with traditional approaches, lies in the fact that not an external object belonging to a person is identified, but the person himself. The most widespread technology of personal identification by fingerprints, which is based on the uniqueness for each person of the pattern of papillary patterns. In recent years, many algorithms and models have appeared to improve the accuracy of the recognition system. The modern algorithms (methods) for the classification of fingerprints are analyzed. Algorithms for the classification of fingerprint images by the types of fingerprints based on the Gabor filter, wavelet - Haar, Daubechies transforms and multilayer neural network are proposed. Numerical and results of the proposed experiments of algorithms are carried out. It is shown that the use of an algorithm based on the combined application of the Gabor filter, a five-level wavelet-Daubechies transform and a multilayer neural network makes it possible to effectively classify fingerprints.

scholarly journals Parametric Convolutional Neural Network for Radar-based Human Activity Classification Using Raw ADC Data

2020 ◽  
Author(s):  
Thomas Stadelmayer ◽  
Avik Santra
Keyword(s):  
Neural Network ◽  
Human Activity ◽  
Recognition System ◽  
Radar Data ◽  
Fast Time ◽  
Activity Classification ◽  
Radar Sensors ◽  
Equivalent State ◽  
Machine Interface

Radar sensors offer a promising and effective sensing modality for<br>human activity classification. Human activity classification enables several smart<br>homes applications for energy saving, human-machine interface for gesture<br>controlled appliances and elderly fall-motion recognition. Present radar-based<br>activity recognition system exploit micro-Doppler signature by generating Doppler<br>spectrograms or video of range-Doppler images (RDIs), followed by deep neural<br>network or machine learning for classification. Although, deep convolutional neural<br>networks (DCNN) have been shown to implicitly learn features from raw sensor<br>data in other fields, such as camera and speech, yet for the case of radar DCNN<br>preprocessing followed by feature image generation, such as video of RDI or<br>Doppler spectrogram, is required to develop a scalable and robust classification<br>or regression application. In this paper, we propose a parametric convolutional<br>neural network that mimics the radar preprocessing across fast-time and slow-time<br>radar data through 2D sinc filter or 2D wavelet filter kernels to extract features for<br>classification of various human activities. It is demonstrated that our proposed<br>solution shows improved results compared to equivalent state-of-art DCNN solutions<br>that rely on Doppler spectrogram or video of RDIs as feature images.

scholarly journals CAPSULE NETWORK BASED BIOMETRIC RECOGNITION SYSTEM

2019 ◽  
Vol 2019 (2) ◽  
pp. 83-94 ◽  
Author(s):  
Dr. I. Jeena Jacob
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Recognition System ◽  
Noise Removal ◽  
Personal Identification ◽  
Biometric Recognition ◽  
Learning Techniques ◽  
Frame Work ◽  
The Stability

The biometric recognition plays a significant and a unique part in the applications that are based on the personal identification. This is because of the stability, irreplaceability and the uniqueness that is found in the biometric traits of the humans. Currently the deep learning techniques that are capable of strongly generalizing and automatically learning, with the enhanced accuracy is utilized for the biometric recognition to develop an efficient biometric system. But the poor noise removal abilities and the accuracy degradation caused due to the very small disturbances has made the conventional means of the deep learning that utilizes the convolutional neural network incompatible for the biometric recognition. So the capsule neural network replaces the CNN due to its high accuracy in the recognition and the classification, due to its learning capacities and the ability to be trained with the limited number of samples compared to the CNN (convolutional neural network). The frame work put forward in the paper utilizes the capsule network with the fuzzified image enhancement for the retina based biometric recognition as it is a highly secure and reliable basis of person identification as it is layered behind the eye and cannot be counterfeited. The method was tested with the dataset of face 95 database and the CASIA-Iris-Thousand, and was found to be 99% accurate with the error rate convergence of 0.3% to .5%

scholarly journals A Review of Iris Recognition Algorithms

2017 ◽  
Vol 1 (4-2) ◽  
pp. 175
Author(s):  
Abdulrahman Aminu Ghali ◽  
Sapiee Jamel ◽  
Kamaruddin Malik Mohamad ◽  
Nasir Abubakar Yakub ◽  
Mustafa Mat Deris
Keyword(s):  
Iris Recognition ◽  
Hamming Distance ◽  
Gabor Filter ◽  
Recognition System ◽  
Personal Identification ◽  
Border Control ◽  
Biometric System ◽  
Rubber Sheet ◽  
Automated Teller Machines ◽  
Recognition Algorithms

With the prominent needs for security and reliable mode of identification in biometric system. Iris recognition has become reliable method for personal identification nowadays. The system has been used for years in many commercial and government applications that allow access control in places such as office, laboratory, armoury, automated teller machines (ATMs), and border control in airport. The aim of the paper is to review iris recognition algorithms. Iris recognition system consists of four main stages which are segmentation, normalization, feature extraction and matching. Based on the findings, the Hough transform, rubber sheet model, wavelet, Gabor filter, and hamming distance are the most common used algorithms in iris recognition stages.  This shows that, the algorithms have the potential and capability to enhanced iris recognition system. 

scholarly journals Convolutional Neural Network (CNN) based Gait Recognition System using Microsoft Kinect Skeleton Features

2018 ◽  
Vol 7 (4.11) ◽  
pp. 202 ◽  
Author(s):  
Mohd Shahrum Md Guntor ◽  
Rohilak Sahak ◽  
Azlee Zabidi ◽  
Nooritawati Md Tahir ◽  
Ihsan Mohd Yassin ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Input Data ◽  
Gait Recognition ◽  
Human Identification ◽  
Recognition System ◽  
Microsoft Kinect ◽  
Biometric Identification ◽  
Oblique View ◽  
Data Points

Biometric identification systems have recently made exponential advancements in term of complexity and accuracy in recognition for security purposes and a variety of other application. In this paper, a Convolutional Neural Network (CNN) based gait recognition system using Microsoft Kinect skeletal joint data points is proposed for human identification. A total of 23 subjects were used for the experiments. The subjects were positioned 45 degrees (oblique view) from Kinect. A CNN based on the modified AlexNet structure was used to fit the different input data size. The results indicate that the training and testing accuracies were 100% and 69.6% respectively.  

scholarly journals Intelligent system for object recognition on optical images using cascade neural networks

2018 ◽  
pp. 50-55 ◽  
Author(s):  
M. V. Shavranskyi ◽  
A. V. Kuchmystenko
Keyword(s):  
Neural Network ◽  
Oil And Gas ◽  
Intelligent System ◽  
Oil And Gas Industry ◽  
Recognition System ◽  
Structure Model ◽  
Gas Industry ◽  
Emergency Situations ◽  
Optical Images

The paper is devoted to increasing the accuracy of the classification of objects on optical images by developing a structure, model and method of teaching the combined neural network and creating on its basis an intelligent image recognition system for tasks of the oil and gas industry - diagnostics, forecasting of emergency situations of technological objects.

scholarly journals Identification of Premature Ventricular Contraction (PVC) Based on ECG Using Convolutional Neural Network

2020 ◽  
Vol 9 (1) ◽  
pp. 2173-2177
Keyword(s):  
Neural Network ◽  
Performance Measures ◽  
Premature Ventricular Contraction ◽  
High Accuracy ◽  
Vital Role ◽  
Ventricular Contraction ◽  
Effective Technique ◽  
Ecg Signals ◽  
Traditional Approaches

Premature Ventricular Contraction (PVC) arrhythmia patients are subjected to dangerous heart rhythms that can be chaotic, and possibly result in abrupt death. Therefore, early detection of arrhythmia with high accuracy is extremely important to detect cardiovascular diseases. The classification of heartbeats based on ECG signals plays a vital role it the field of cardiac sciences to identify arrhythmias. The use of Artificial Neural Networks (ANN) has proven to be the most effective technique for sole agenda of classification. The use of CNN is simple and more noise immune method in comparison to various other techniques. In this paper, a survey of numerous algorithms and classification techniques along with their performance measures are presented. This paper proposes the identification of PVC on the basis of heart beats by using CNN and the results obtained are compared to other traditional approaches

scholarly journals A NEURAL NETWORK BASED IRIS RECOGNITION SYSTEM FOR PERSONAL IDENTIFICATION

2010 ◽  
Vol 1 (2) ◽  
pp. 78-84 ◽  
Author(s):  
Usham Dias ◽  
◽  
Vinita Frietas ◽  
Sandeep P S ◽  
Amanda Fernandes ◽  
...  
Keyword(s):  
Neural Network ◽  
Iris Recognition ◽  
Recognition System ◽  
Personal Identification

scholarly journals Skin Disease Recognition using Texture Analysis

2021 ◽  
Vol 9 (VI) ◽  
pp. 1560-1562
Author(s):  
Prof. M. G. Panjwani
Keyword(s):  
Neural Network ◽  
Side Effects ◽  
Texture Analysis ◽  
Recognition System ◽  
Image Feature ◽  
Skin Infections ◽  
Image Feature Extraction ◽  
Long Time ◽  
Illness Recognition

Skin is the primary part of our body, One of the major issues we are facing presently days that's skin illness due to high air pollution. In this research, we are trying to skin illness recognition by using Neural Network which is based on texture analysis. There are many skin infections like Eczema, Acne, Hives, rosacea, psoriasis, etc. In common, these diseases have similarities in the design of contamination and side effects such as redness and rash. Diagnosis and recognition of skin illness take a really long time to handle. The infection determination and recognition gets to be troublesome as the complexity and number of highlights of the infection increases. Thus, a computer helped diagnosis and recognition system is presented. Computer algorithm which contains few steps that are image processing, image feature extraction, segmentation, and classification .of information has been executed with the assistance of a Convolutional neural network (CNN). The CNN can learn designs of side effects of specific infections and makes it speedier.

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