scholarly journals Comparison between Physiological and Behavioral Characteristics of Biometric System

2019 ◽  
Vol 54 (6) ◽  
Author(s):  
Azal Habeeb
Keyword(s):  
Iris Recognition ◽  
Voice Recognition ◽  
Technical Aspect ◽  
Vital Role ◽  
Biometric System ◽  
Behavioral Biometrics ◽  
Canny Edge ◽  
Mel Frequency Cepstral Coefficient ◽  
The Voice ◽  
Recognition Technique

Biometrics is a technical aspect to identify each person from others. It is one of the ways to distinguish a person’s identity. The biometric system plays a vital role in data security. There are two types of biometric systems, i.e., physiological and behavioral biometrics. Physiological biometrics involves the fingerprint, iris, and face, while behavioral biometrics includes the signature, stroke, and voice. This paper discussed the iris recognition technique using the Canny edge detector and Hough transform to separate iris region from the eye images. The voice recognition technique was discussed using mel-frequency cepstral coefficient (MFCC) method. Finally, the paper compared iris recognition and voice recognition according to their properties and their performance.

A Review on Biometric Technology

2018 ◽  
Vol 8 (4) ◽  
pp. 101
Author(s):  
Kartik Choudhary ◽  
Rizwan Khan
Keyword(s):  
Face Recognition ◽  
Iris Recognition ◽  
Review Paper ◽  
Behavioral Characteristics ◽  
Biometric System ◽  
Biometric Technology ◽  
The Face ◽  
The Individual ◽  
Recognition Technique ◽  
Better Than

Biometric Technology has turned out to be a popular area of research in computer vision and one of the most successful applications for identifying humans by capturing and analysing the sole feature or characteristic of   individual which is possessed by them and involves their Physical and Behavioral characteristics. For the individual validation and authentication the biometric system has this responsibility. Biometric Technology started from the fingerprints recognition and later on improvements were done in it to make it more secure which involves the face recognition and iris Recognition. Almost both of them are available and regarded as the accurate and reliable technology for biometric validation system. This review paper is all about Face recognition techniques in biometric locking system and Iris recognition technique of identification and the ways of making locking systems ways more efficient, full of ease, more secure, and far better than before so as to make locking or security stronger. It discusses about face recognition technique, its working and its application in different sector along with iris recognition, its working, its application.

scholarly journals A wireless controlled intelligent healthcare system for diplegia patients

2021 ◽  
Vol 19 (1) ◽  
pp. 456-472
Author(s):  
Muhammad Tanveer Riaz ◽  
◽  
Abeer Abdulaziz AlSanad ◽  
Saeed Ahmad ◽  
Muhammad Azeem Akbar ◽  
...  
Keyword(s):  
Healthcare System ◽  
Voice Recognition ◽  
Recognition System ◽  
Vital Role ◽  
The Body ◽  
Low Light ◽  
The Common ◽  
User Friendly ◽  
The Voice ◽  
Intelligent Healthcare

<abstract> <p>Rehabilitation engineering is playing a more vital role in the field of healthcare for humanity. It is providing many assistive devices to diplegia patients (The patients whose conditions are weak in terms of muscle mobility on both sides of the body and their paralyzing effects are high either in the arms or in the legs). Therefore, in order to rehabilitate such types of patients, an intelligent healthcare system is proposed in this research. The electric sticks and chairs are also a type of this system which was used previously to facilitate the diplegia patients. It is worth noting that a voice recognition system along with wireless control feature has been integrated intelligently in the proposed healthcare system in order to replace the common and conventional assistive tools for diplegia patients. These features will make the proposed system more user friendly, convenient and comfortable. The voice recognition system has been used for movements of system in any desired direction along with the ultrasonic sensor and light detecting technology. These sensors detect the obstacles and low light environment intelligently during the movement of the wheelchair and then take the necessary actions accordingly.</p> </abstract>

Voice Recognition for STEM Education Using Robotics

2017 ◽  
Author(s):  
Shubo Chen ◽  
Binsen Qian ◽  
Harry Cheng
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Stem Education ◽  
Basic Concept ◽  
Voice Recognition ◽  
Voice Control ◽  
K 12 ◽  
The Voice ◽  
Recognition Technique ◽  
Machine Learning Tool

In this paper, we provide a new voice recognition framework which allows K-12 students to write programs to solve problems using voice control. The framework contains the voice recognition module SPHINX which is based on an open source machine learning tool developed by Carnegie Mellon University and a wrapper function which is written in C/C++ interpreter Ch. The wrapper function allows students to interact the module in Ch. Along with Ch programming and robotic coursework, students will get the chance to learn the basic concept of machine learning and voice recognition technique. In order to bring students attention and interest in machine learning, various tasks have been designed for students to accomplish based on the framework. The framework is also flexible for them to explore other interesting projects.

scholarly journals Iris segmentation using a new unsupervised neural approach

2020 ◽  
Vol 9 (1) ◽  
pp. 58
Author(s):  
Hicham Ohmaid ◽  
S. Eddarouich ◽  
A. Bourouhou ◽  
M. Timouyas
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Iris Recognition ◽  
Recognition System ◽  
Automatic Recognition ◽  
Biometric Identification ◽  
Iris Segmentation ◽  
Biometric System ◽  
Canny Edge ◽  
Epigenetic Patterns

<span lang="EN-GB">A biometric system of identification and authentication provides automatic recognition of an individual based on certain unique features or characteristic possessed by an individual. Iris recognition is a biometric identification method that uses pattern recognition on the images of the iris. Owing to the unique epigenetic patterns of the iris, Iris recognition is considered as one of the most accurate methods in the field of biometric identification. One of the crucial steps in the iris recognition system is the iris segmentation because it significantly affects the accuracy of the feature extraction the iris. The segmentation algorithm proposed in this article starts with determining the regions of the eye using unsupervised neural approach, after the outline of the eye is found using the Canny edge, The Hough Transform is employed to determine the </span><span lang="EN-US">center</span><span lang="EN-GB"> and radius of the pupil and the iris.</span>

Lampu Sein Helm Sepeda Berbasis Voice Recognition

2019 ◽  
Vol 11 (01) ◽  
pp. 20-25
Author(s):  
Indra Saputra ◽  
Parulian Silalahi ◽  
Bayu Cahyawan ◽  
Imam Akbar
Keyword(s):  
Voice Recognition ◽  
Driving Safety ◽  
Bicycle Helmet ◽  
Turn Signal ◽  
Voice Command ◽  
The Voice ◽  
Indicator Signal

Bicycles are not equipped with the turn signal. For driving safety, a bicycle helmet with a turn signal is designed with voice rrecognition. It is using the Arduino Nano as a controller to control the ON and OFF of turn signal lights with voice commands. This device uses a Voice Recognition sensor and microphone that placed on a bicycle helmet. When the voice command is mentioned in the microphone, the Voice Recognition sensor will detect the command specified, the sensor will automatically read and send a signal to Arduino, then the turn signal will light up as instructed, the Arduino on the helmet will send an indicator signal via the Bluetooth Module. The device is able to detect sound with a percentage of 80%. The tool can work with a distance of <2 meters with noise <71 db.

ANALYSIS OF VOICE CUES IN RECOGNITION OF SARCASM

2019 ◽  
Vol 12 ◽  
Author(s):  
Basavaraj N Hiremath ◽  
Malini M Patilb
Keyword(s):  
Feature Extraction ◽  
Computer Program ◽  
Principle Component Analysis ◽  
Voice Recognition ◽  
Component Analysis ◽  
Recognition System ◽  
Principle Component ◽  
Whole Process ◽  
The Voice

The voice recognition system is about cognizing the signals, by feature extraction and identification of related parameters. The whole process is referred to as voice analytics. The paper aims at analysing and synthesizing the phonetics of voice using a computer program called “PRAAT”. The work carried out in the paper also supports the analysis of voice segmentation labelling, analyse the unique features of voice cues, understanding physics of voice, further the process is carried out to recognize sarcasm. Different unique features identified in the work are, intensity, pitch, formants related to read, speak, interactive and declarative sentences by using principle component analysis.

Voice Assistant — Application of Speech Recognition Technology in the Android System

2014 ◽  
Vol 596 ◽  
pp. 384-387
Author(s):  
Ge Liu ◽  
Hai Bing Zhang
Keyword(s):  
Speech Recognition ◽  
Service Providers ◽  
Voice Recognition ◽  
Working Process ◽  
Bottleneck Problems ◽  
Speech Recognition Technology ◽  
Android System ◽  
The Voice

This paper introduces the concept of Voice Assistant, the voice recognition service providers, several typical Voice Assistant product, and then the basic working process of the Voice Assistant is described in detail and proposed the technical bottleneck problems in the development of Voice Assistant software.

scholarly journals Design and Fabrication of Voice Controlled Unmanned Aerial Vehicle

2016 ◽  
Vol 5 (3) ◽  
pp. 205
Author(s):  
S. Sakthi Anand ◽  
R. Mathiyazaghan
Keyword(s):  
Control System ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Voice Recognition ◽  
Search And Rescue ◽  
Voice Input ◽  
Recognition Process ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
The Voice

<p class="Default">Unmanned Aerial Vehicles have gained well known attention in recent years for a numerous applications such as military, civilian surveillance operations as well as search and rescue missions. The UAVs are not controlled by professional pilots and users have less aviation experience. Therefore it seems to be purposeful to simplify the process of aircraft controlling. The objective is to design, fabricate and implement an unmanned aerial vehicle which is controlled by means of voice recognition. In the proposed system, voice commands are given to the quadcopter to control it autonomously. This system is navigated by the voice input. The control system responds to the voice input by voice recognition process and corresponding algorithms make the motors to run at specified speeds which controls the direction of the quadcopter.</p>

scholarly journals Biometric Systems De-Identification: Current Advancements and Future Directions

2021 ◽  
Vol 1 (3) ◽  
pp. 470-495
Author(s):  
Md Shopon ◽  
Sanjida Nasreen Tumpa ◽  
Yajurv Bhatia ◽  
K. N. Pavan Kumar ◽  
Marina L. Gavrilova
Keyword(s):  
Information Security ◽  
System Development ◽  
User Profile ◽  
Information Privacy ◽  
Biometric System ◽  
Comprehensive Overview ◽  
Future Directions ◽  
Behavioral Biometrics ◽  
Open Questions ◽  
Biometric Systems

Biometric de-identification is an emerging topic of research within the information security domain that integrates privacy considerations with biometric system development. A comprehensive overview of research in the context of authentication applications spanning physiological, behavioral, and social-behavioral biometric systems and their privacy considerations is discussed. Three categories of biometric de-identification are introduced, namely complete de-identification, auxiliary biometric preserving de-identification, and traditional biometric preserving de-identification. An overview of biometric de-identification in emerging domains such as sensor-based biometrics, social behavioral biometrics, psychological user profile identification, and aesthetic-based biometrics is presented. The article concludes with open questions and provides a rich avenue for subsequent explorations of biometric de-identification in the context of information privacy.

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