scholarly journals An Enhanced IRIS Output Technique (EIOT) for Biometric Security

2019 ◽  
Vol 9 (2) ◽  
pp. 4788-4791
Keyword(s):  
Iris Recognition ◽  
Human Identification ◽  
Identification System ◽  
Security Systems ◽  
Security Applications ◽  
Biometric Security ◽  
Reliable Systems ◽  
Human Eyes ◽  
Biometric Features

Human biometric features form the base for many security applications which identify humans uniquely. Human eyes and specifically the Iris based identifications are regarded as highly reliable systems. Iris based systems when combined with cryptography have been able to present higher biometric based security systems. This paper presents an Iris based human identification system called EIOT which can enhance biometric security. It is a set of unique sequential steps followed in Iris recognition and can be implemented in human authentications and identifications.

Biometric Security

2015 ◽  
pp. 199-217
Author(s):  
Muzhir Shaban Al-Ani
Keyword(s):  
Biological Sciences ◽  
Data Analysis ◽  
Personal Identification ◽  
Biometric System ◽  
Mathematical Methods ◽  
Security Systems ◽  
Biometric Security ◽  
The Future ◽  
Behavioral Parameters ◽  
Biometric Features

The terms biometrics and biometry have been used to refer to the field of development of statistical and mathematical methods applicable to data analysis problems in the biological sciences. Recently biometrics refers to technologies and applications applied for personal identification using physical and behavioral parameters. Biometric security systems ensuring that only the authorized persons are permitted to access a certain data, because it is difficult to copy the biometric features pattern for a specific person. Biometrics is playing an important role in applications that are centric on identification, verification and classification. This chapter focuses on biometric security in their types, specifications, technologies and algorithms. Some algorithms of biometric security are also included in this chapter. Finally latest and future aspects of biometric system and merging technologies are also mentioned, including more details of system structures and specifications and what constitution will shape biometric security of in the future.

Biometric Security

Biometrics ◽  
2017 ◽  
pp. 1399-1418
Author(s):  
Muzhir Shaban Al-Ani
Keyword(s):  
Biological Sciences ◽  
Data Analysis ◽  
Personal Identification ◽  
Biometric System ◽  
Mathematical Methods ◽  
Security Systems ◽  
Biometric Security ◽  
The Future ◽  
Behavioral Parameters ◽  
Biometric Features

The terms biometrics and biometry have been used to refer to the field of development of statistical and mathematical methods applicable to data analysis problems in the biological sciences. Recently biometrics refers to technologies and applications applied for personal identification using physical and behavioral parameters. Biometric security systems ensuring that only the authorized persons are permitted to access a certain data, because it is difficult to copy the biometric features pattern for a specific person. Biometrics is playing an important role in applications that are centric on identification, verification and classification. This chapter focuses on biometric security in their types, specifications, technologies and algorithms. Some algorithms of biometric security are also included in this chapter. Finally latest and future aspects of biometric system and merging technologies are also mentioned, including more details of system structures and specifications and what constitution will shape biometric security of in the future.

scholarly journals IRIS TECHNOLOGY: A REVIEW ON IRIS BASED BIOMETRIC SYSTEMS FOR UNIQUE HUMAN IDENTIFICATION

2018 ◽  
Vol 6 (1) ◽  
pp. 80-90
Author(s):  
M V Bramhananda Reddy ◽  
V Goutham
Keyword(s):  
Iris Recognition ◽  
Human Identification ◽  
Smart Devices ◽  
Biometric Identification ◽  
Great Precision ◽  
Iris Localization ◽  
Biometric Systems ◽  
Real Time Applications ◽  
Multimodal Biometric Systems ◽  
Biometric Features

Biometric features are widely used in real time applications for unique human identification. Iris is one of the physiological biometric features which are regarded as highly reliable in biometric identification systems. Often iris is combined with other biometric features for robust biometric systems. It is also observed that biometrics is combined with cryptography for stronger security mechanisms. Since iris is unique for all individuals across the globe, many researchers focused on using iris or along with other biometrics for security with great precision. Multimodal biometric systems came into existence for better accuracy in human authentication. However, iris is considered to be most discriminatory of facial biometrics. Study of iris based human identification in ideal and non-cooperative environments can provide great insights which can help researchers and organizations that depend on iris-based biometric systems. The technical knowhow of iris strengths and weaknesses can be great advantage. This is more important in the wake of widespread use of smart devices which are vulnerable to attacks. This paper throws light into various iris-based biometric systems, issues with iris in the context of texture comparison, cancellable biometrics, iris in multi-model biometric systems, iris localization issues, challenging scenarios pertaining to accurate iris recognition and so on.

Evaluation of the effectiveness of different image skeletonization methods in biometric security systems

2020 ◽  
Vol 10 ◽  
Author(s):  
Mariya Nazarkevych ◽  
Serhii Dmytruk ◽  
Volodymyr Hrytsyk ◽  
Olha Vozna ◽  
Anzhela Kuza ◽  
...  
Keyword(s):  
Experimental Studies ◽  
Fingerprint Recognition ◽  
Good Time ◽  
Gabor Filtering ◽  
Security Systems ◽  
Thinning Algorithm ◽  
Biometric Security ◽  
Recognition Quality ◽  
Control And Management ◽  
The Internet Of Things

Background: Systems of the Internet of Things are actively implementing biometric systems. For fast and high-quality recognition in sensory biometric control and management systems, skeletonization methods are used at the stage of fingerprint recognition. The analysis of the known skeletonization methods of Zhang-Suen, Hilditch, Ateb-Gabor with the wave skeletonization method has been carried out and it shows a good time and qualitative recognition results. Methods: The methods of Zhang-Suen, Hildich and thinning algorithm based on Ateb-Gabor filtration, which form the skeletons of biometric fingerprint images, are considered. The proposed thinning algorithm based on Ateb-Gabor filtration showed better efficiency because it is based on the best type of filtering, which is both a combination of the classic Gabor function and the harmonic Ateb function. The combination of this type of filtration makes it possible to more accurately form the surroundings where the skeleton is formed. Results: Along with the known ones, a new Ateb-Gabor filtering algorithm with the wave skeletonization method has been developed, the recognition results of which have better quality, which allows to increase the recognition quality from 3 to 10%. Conclusion: The Zhang-Suen algorithm is a 2-way algorithm, so for each iteration, it performs two sets of checks during which pixels are removed from the image. Zhang-Suen's algorithm works on a plot of black pixels with eight neighbors. This means that the pixels found along the edges of the image are not analyzed. Hilditch thinning algorithm occurs in several passages, where the algorithm checks all pixels and decides whether to replace a pixel from black to white if certain conditions are satisfied. This Ateb-Gabor filtering will provide better performance, as it allows to obtain more hollow shapes, organize a larger range of curves. Numerous experimental studies confirm the effectiveness of the proposed method.

USABLE SECURITY IN BIOMETRIC AUTHENTICATION SYSTEMS

2021 ◽  
Vol 17 (1) ◽  
pp. 287-292
Author(s):  
Adriana-Meda UDROIU ◽  
Ștefan-Antonio DAN-ȘUTEU
Keyword(s):  
Iris Recognition ◽  
Accurate Method ◽  
Biometric Authentication ◽  
Usable Security ◽  
Security Systems ◽  
Field Methods ◽  
Secure Systems ◽  
Authentication System ◽  
Secure Authentication ◽  
Pattern Variability

Abstract: We introduce the term usable security to refer to security systems, models, mechanisms and applications that have as the main goal usability. Secure systems cannot exist without secure authentication methods. Thus we outline biometric authentication methods and we focus on iris recognition because is the most reliable and accurate method for human identification]. The most important advantage of iris biometric over other biometrics is that irises have enormous pattern variability meaning that the variation between individual is almost maximum and variation for any person across time or conditions is minimum. Taking into consideration this observations, this survey covers researches in this field, methods of technical implementation and the usability of this method as an authentication system on iOS environment.

GaitSense: Towards Ubiquitous Gait-Based Human Identification with Wi-Fi

2022 ◽  
Vol 18 (1) ◽  
pp. 1-24
Author(s):  
Yi Zhang ◽  
Yue Zheng ◽  
Guidong Zhang ◽  
Kun Qian ◽  
Chen Qian ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Gait Recognition ◽  
Wearable Sensors ◽  
Human Identification ◽  
Training Data ◽  
Identification Accuracy ◽  
Identification System ◽  
Gait Patterns ◽  
Training Samples ◽  
Augmentation Techniques

Gait, the walking manner of a person, has been perceived as a physical and behavioral trait for human identification. Compared with cameras and wearable sensors, Wi-Fi-based gait recognition is more attractive because Wi-Fi infrastructure is almost available everywhere and is able to sense passively without the requirement of on-body devices. However, existing Wi-Fi sensing approaches impose strong assumptions of fixed user walking trajectories, sufficient training data, and identification of already known users. In this article, we present GaitSense , a Wi-Fi-based human identification system, to overcome the above unrealistic assumptions. To deal with various walking trajectories and speeds, GaitSense first extracts target specific features that best characterize gait patterns and applies novel normalization algorithms to eliminate gait irrelevant perturbation in signals. On this basis, GaitSense reduces the training efforts in new deployment scenarios by transfer learning and data augmentation techniques. GaitSense also enables a distinct feature of illegal user identification by anomaly detection, making the system readily available for real-world deployment. Our implementation and evaluation with commodity Wi-Fi devices demonstrate a consistent identification accuracy across various deployment scenarios with little training samples, pushing the limit of gait recognition with Wi-Fi signals.

scholarly journals Behavioral Biometric Data Analysis for Gender Classification Using Feature Fusion and Machine Learning

2021 ◽  
Vol 8 ◽  
Author(s):  
Shivanand S. Gornale ◽  
Sathish Kumar ◽  
Abhijit Patil ◽  
Prakash S. Hiremath
Keyword(s):  
Machine Learning ◽  
Feature Fusion ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Gender Classification ◽  
Biometric Data ◽  
Security Applications ◽  
Biometric Security ◽  
Learning Techniques ◽  
Handwritten Signature

Biometric security applications have been employed for providing a higher security in several access control systems during the past few years. The handwritten signature is the most widely accepted behavioral biometric trait for authenticating the documents like letters, contracts, wills, MOU’s, etc. for validation in day to day life. In this paper, a novel algorithm to detect gender of individuals based on the image of their handwritten signatures is proposed. The proposed work is based on the fusion of textural and statistical features extracted from the signature images. The LBP and HOG features represent the texture. The writer’s gender classification is carried out using machine learning techniques. The proposed technique is evaluated on own dataset of 4,790 signatures and realized an encouraging accuracy of 96.17, 98.72 and 100% for k-NN, decision tree and Support Vector Machine classifiers, respectively. The proposed method is expected to be useful in design of efficient computer vision tools for authentication and forensic investigation of documents with handwritten signatures.

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