Ear biometric verification approach based on morphological and geometric invariants

2020 ◽  
Vol 20 (3) ◽  
pp. 1479
Author(s):  
Wurood A. Jbara
Keyword(s):  
Feature Extraction ◽  
Edge Detection ◽  
Main Idea ◽  
High Accuracy ◽  
Geometric Invariants ◽  
Biometric Verification ◽  
Accuracy Level ◽  
Image Capturing ◽  
Verification Process ◽  
Biometric Model

<p>Biometric verification based on ear features is modern filed for scientific research. As known, there are many biometric identifiers that can identify people such as fingerprints, iris and speech. In this paper, the focus is placed on the ear biometric model in order to verifying the identity of persons. The main idea is based on used the moments as ear feature extractors. The proposed approach included some operations as follow: image capturing, edge detection, erosion, feature extraction, and matching. The proposed approach has been tested using many images of the ears with different states. Experimental results using several trails verified that the proposed approach is achieved high accuracy level over a wide variety of ear images. Also, the verification process will be completed by matching query ear image with ear images that kept in database during real time.</p>

scholarly journals Beyond Speech: Generalizing D-Vectors for Biometric Verification

2019 ◽  
Vol 33 ◽  
pp. 842-849 ◽  
Author(s):  
Jacob Baldwin ◽  
Ryan Burnham ◽  
Andrew Meyer ◽  
Robert Dora ◽  
Robert Wright
Keyword(s):  
Feature Extraction ◽  
Cyber Security ◽  
Speaker Identification ◽  
State Of The Art ◽  
Extraction Methods ◽  
High Accuracy ◽  
Automatic Feature Extraction ◽  
Biometric Verification ◽  
Generalized Framework ◽  
Data Requirements

Deep learning based automatic feature extraction methods have radically transformed speaker identification and facial recognition. Current approaches are typically specialized for individual domains, such as Deep Vectors (D-Vectors) for speaker identification. We provide two distinct contributions: a generalized framework for biometric verification inspired by D-Vectors and novel models that outperform current stateof-the-art approaches. Our approach supports substitution of various feature extraction models and improves the robustness of verification tests across domains. We demonstrate the framework and models for two different behavioral biometric verification problems: keystroke and mobile gait. We present a comprehensive empirical analysis comparing our framework to the state-of-the-art in both domains. Our models perform verification with higher accuracy using orders of magnitude less data than state-of-the-art approaches in both domains. We believe that the combination of high accuracy and practical data requirements will enable application of behavioral biometric models outside of the laboratory in support of much-needed improvements to cyber security.

scholarly journals Design and Development of a 5-Channel Arduino-Based Data Acquisition System (ABDAS) for Experimental Aerodynamics Research

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2382 ◽  
Author(s):  
Antonio Vidal-Pardo ◽  
Santiago Pindado
Keyword(s):  
Data Acquisition ◽  
Low Cost ◽  
Data Acquisition System ◽  
High Accuracy ◽  
Acquisition System ◽  
Calibration System ◽  
Experimental Aerodynamics ◽  
Accuracy Level ◽  
The Fourier Transform ◽  
Cup Anemometer

In this work, a new and low-cost Arduino-Based Data Acquisition System (ABDAS) for use in an aerodynamics lab is developed. Its design is simple and reliable. The accuracy of the system has been checked by being directly compared with a commercial and high accuracy level hardware from National Instruments. Furthermore, ABDAS has been compared to the accredited calibration system in the IDR/UPM Institute, its measurements during this testing campaign being used to analyzed two different cup anemometer frequency determination procedures: counting pulses and the Fourier transform. The results indicate a more accurate transfer function of the cup anemometers when counting pulses procedure is used.

scholarly journals MULTI-CHANNEL FRY COUNTER DESIGN USING OPTOCOUPLER SENSOR

2019 ◽  
Vol 4 (2) ◽  
pp. 97-104
Author(s):  
Lazuardi Umar ◽  
Yanuar Hamzah ◽  
Rahmondia N. Setiadi
Keyword(s):  
Counting Process ◽  
Production Capacity ◽  
High Accuracy ◽  
Limited Capacity ◽  
High Concentration ◽  
Counting System ◽  
Channel Detection ◽  
Accuracy Level ◽  
High Level ◽  
Almost All

This paper describes a design of a fry counter intended to be used by consuming fish farmer. Along this time, almost all the fry counting process is counted by manual, which is done by a human. It is requiring much energy and needs high concentration; thus, can cause a high level of exhaustion for the fry counting worker. Besides that, the human capability and capacity of counting are limited to a low level. A fry counter design in this study utilizes a multi-channel optocoupler sensor to increase the counting capacity. The multi-channel fry counter counting system is developed as a solution to a limited capacity of available fry counter. This design uses an input signal extender system on controller including the interrupt system. From the experiment, high accuracy level is obtained on the counting and channel detection, therefore, this design can be implemented and could help farmers to increase the production capacity of consuming fish.

Snickometer Edge Detection by Feature Extraction in TF Plane and Wavelet Domain

2018 ◽  
Author(s):  
Avrajit Ghosh ◽  
Ayan Chatterjee ◽  
Arani Roy ◽  
Amitava Mukherjee ◽  
Mrinal Kanti Naskar
Keyword(s):  
Feature Extraction ◽  
Edge Detection ◽  
Wavelet Domain

scholarly journals Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

2020 ◽  
Vol 10 (20) ◽  
pp. 7068
Author(s):  
Minh Tuan Pham ◽  
Jong-Myon Kim ◽  
Cheol Hong Kim
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Extraction Methods ◽  
High Accuracy ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Ae Signals

Recent convolutional neural network (CNN) models in image processing can be used as feature-extraction methods to achieve high accuracy as well as automatic processing in bearing fault diagnosis. The combination of deep learning methods with appropriate signal representation techniques has proven its efficiency compared with traditional algorithms. Vital electrical machines require a strict monitoring system, and the accuracy of these machines’ monitoring systems takes precedence over any other factors. In this paper, we propose a new method for diagnosing bearing faults under variable shaft speeds using acoustic emission (AE) signals. Our proposed method predicts not only bearing fault types but also the degradation level of bearings. In the proposed technique, AE signals acquired from bearings are represented by spectrograms to obtain as much information as possible in the time–frequency domain. Feature extraction and classification processes are performed by deep learning using EfficientNet and a stochastic line-search optimizer. According to our various experiments, the proposed method can provide high accuracy and robustness under noisy environments compared with existing AE-based bearing fault diagnosis methods.

A Hybrid Analytical and Finite Element Method for Mid-Frequency Vibration Analysis of Plate Structures with Discontinuities

2017 ◽  
Vol 17 (04) ◽  
pp. 1750052 ◽  
Author(s):  
Yongbin Ma ◽  
Yahui Zhang ◽  
Bo Ping Wang
Keyword(s):  
Finite Element ◽  
Vibration Analysis ◽  
Present Method ◽  
High Efficiency ◽  
Main Idea ◽  
High Accuracy ◽  
Symplectic Method ◽  
Rectangular Plates ◽  
Fe Method ◽  
Plate Structures

A hybrid analytical and numerical method is presented for the mid-frequency vibration analysis of a class of plate structures with discontinuities based on the concept of structural partitioning. The type of structures considered includes rectangular plates with internal property discontinuity, homogeneous but non-rectangular plates, or built-up structures composed of rectangular homogenous plates with complex joints. Compared with the conventional finite element (FE) method, the present method has the advantage of high accuracy and high efficiency in the analysis of mid-frequency vibration of the structures of concern. The main idea of the proposed approach is to divide the whole structure into uniform rectangular plate regions and other non-rectangular regions. The vibration behavior of the rectangular regions is accurately and efficiently described by analytical wave solutions so that the FE modeling for these regions is not necessary. The other non-rectangular regions are modeled by the conventional FE method. The analytical waves used to describe the rectangular regions are obtained by the symplectic method, thereby avoiding the limitation of the conventional analytical method in dealing with plates having two opposite edges simply supported. By enforcing the displacement continuity and force equilibrium at the connection interfaces, the dynamic coupling is established between the rectangular regions described in terms of the analytical waves and the regions modeled by FEs. Furthermore, the hybrid solution formulation for the mid-frequency vibration of the entire structure is proposed. The high accuracy and efficiency of the present method are demonstrated by several numerical examples, with the effect of element size of the FE regions investigated. Finally, the applicability of the proposed method is analyzed.

Early Image Fire-Detection Based on Maximum Margin Criterion

2014 ◽  
Vol 526 ◽  
pp. 324-329
Author(s):  
Jie Yuan ◽  
Hai Bing Hu ◽  
Wei Yuan ◽  
Yang Jia ◽  
Yong Ming Zhang
Keyword(s):  
Feature Extraction ◽  
Mathematical Description ◽  
Main Idea ◽  
Fire Detection ◽  
Color Model ◽  
Scatter Matrix ◽  
Maximum Margin ◽  
Maximum Margin Criterion ◽  
Early Image ◽  
The Difference

Nowadays as camera is applied widely, image fire detection becomes much popular. Many researchers are committed to analyze the RGB color model or even gray images. Actually they have some disadvantages. So this paper will present a new model based on Maximum Margin Criterion, a feature extraction criterion. As it is maximizing the difference of between-class scatter matrices and within-class scatter matrices, it does not depend on the nonsingularity of the within-class scatter matrix. First we will introduce the main idea and then give a mathematical description to apply the model to fire detection, with the algorithm we can calculate the result we need. At last we will put them into practice, use a database to do some experiments to present the performance of this method.

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