scholarly journals Fingerprint Authentication

2021 ◽  
Vol 10 (5) ◽  
pp. 87-89
Author(s):  
J Paul Rajasingh ◽  
D Sai Yaswanth
Keyword(s):  
Iris Recognition ◽  
Fingerprint Recognition ◽  
Automatic Identification ◽  
Biometric Recognition ◽  
Behavioural Characteristics ◽  
The Face ◽  
Signature Recognition ◽  
Personal Recognition ◽  
Vein Recognition ◽  
Hand Geometry

Biometrics refers to the automatic identification of a living person based on physiological or behavioural characteristics for authentication purpose. Among the existing biometric technologies are the face recognisation, fingerprint recognition, finger-geometry, hand geometry, iris recognition, vein recognition, voice recognition and signature recognition, Biometric method requires the physical presence of the person to be identified. This emphasizes its preference over the traditional method of identifying what you have such as, the use of password, a smartcard etc. Also, it potentially prevents unauthorized admittance to access control systems or fraudulent use of ATMs, Time Attendance Systems, cellular phones, smart cards, desktop PCs, Workstations, vehicles and computer networks. Biometric recognition systems offer greater security and convenience than traditional methods of personal recognition.

Evaluation of Biometric Technologies for Access Control at Transportation Facilities and Border Crossings

2005 ◽  
Vol 1938 (1) ◽  
pp. 1-8
Author(s):  
Yuko J. Nakanishi ◽  
Jeffrey Western
Keyword(s):  
Access Control ◽  
Human Error ◽  
Iris Recognition ◽  
Performance Data ◽  
Border Crossing ◽  
Fingerprint Recognition ◽  
Border Crossings ◽  
Advantages And Disadvantages ◽  
Hand Geometry ◽  
Transportation Facilities

To ensure that only authorized individuals–-legitimate workers, travelers, and visitors–-enter a transportation facility or border crossing, their identities must be ascertained. Because manual procedures are time-consuming, resource intensive, and vulnerable to human error and manipulation, the use of biometric technologies should be considered. This paper discusses several biometric technologies–-fingerprint recognition, iris recognition, facial recognition, and hand geometry–-and assesses their feasibility for use in access control at transportation facilities and border crossings. The advantages and disadvantages of the technologies are provided, as are cost, accuracy, and other performance data. Potential privacy and data issues are also discussed.

scholarly journals A fast iris recognition system through optimum feature extraction

2019 ◽  
Author(s):  
Humayan Kabir Rana ◽  
Md. Shafiul Azam ◽  
Mst. Rashida Akhtar ◽  
Julian M.W. Quinn ◽  
Mohammad Ali Moni
Keyword(s):  
Feature Extraction ◽  
Iris Recognition ◽  
Principal Component ◽  
Recognition System ◽  
Discrete Wavelet ◽  
Automated Identification ◽  
Biometric Recognition ◽  
Efficient Performance ◽  
Increasing Demand ◽  
Hand Geometry

With an increasing demand for stringent security systems, automated identification of individuals based on biometric methods has been a major focus of research and development over the last decade. Biometric recognition analyses unique physiological traits or behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry, keystrokes or gait. The iris has a complex and unique structure that remains stable over a person's lifetime, features that have led to its increasing interest in its use for biometric recognition. In this study, we proposed a technique incorporating Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for the extraction of the optimum features of an iris and reducing the runtime needed for iris templates classification. The idea of using DWT behind PCA is to reduce the resolution of the iris template. DWT converts an iris image into four frequency sub-bands. One frequency sub-band instead of four has been used for further feature extraction by using PCA. Our experimental evaluation demonstrates the efficient performance of the proposed technique.

Biometric Technologies

2011 ◽  
pp. 369-374
Author(s):  
Yingzi ("Eliza") Du
Keyword(s):  
Face Recognition ◽  
Speech Signal ◽  
Iris Recognition ◽  
Voice Recognition ◽  
Human Identification ◽  
Emerging Technology ◽  
Fingerprint Recognition ◽  
Biological Traits ◽  
Signal Characteristics ◽  
Hand Geometry

Biometrics is an emerging technology for automatic human identification and verification using unique biological traits (Woodward, Orlans, & Higgins, 2002). These traits include face, fingerprints, iris, voice, hand geometry, handwriting, retina, and veins. For example, fingerprint recognition analyzes ridge ends, bifurcation, or dots of finger tips; voice recognition analyzes speech signal characteristics; iris recognition analyzes the pits, striations, filaments, rings, dark spots, and freckles of eyes; and face recognition analyzes facial parameters (Du et al., 2004). It is based on “something you are” rather than “something you have” (Du, 2005). Compared to the traditional identification and verification ways, such as user name/password, and paper IDs, biometrics is more convenient to use, reduces fraud, and is more secure (Reid, 2004).

The Application of Biometric Identification Technology in ATM System

2013 ◽  
Vol 347-350 ◽  
pp. 3419-3421
Author(s):  
Ming Hui Zhang ◽  
Yao Yu Zhang
Keyword(s):  
Face Recognition ◽  
Second Generation ◽  
Fingerprint Recognition ◽  
Biometric Identification ◽  
Automatic Identification ◽  
Behavioral Characteristics ◽  
Automatic Teller Machine ◽  
Biological Recognition ◽  
The Face ◽  
Bank System

Biometric identification technology deals with the identification of individuals based on their biological or human behavioral characteristics. Biometric identification method is reliable, anti-counterfeit, convenient and safe. At present there are some insecurity factors in the ATM (automatic teller machine) in bank system. Methods such as combining biological recognition with the ATM machine, adding the face recognition technology, fingerprint recognition, second generation ID card recognition, enhancing automatic identification are developed to improve the security of ATM.

scholarly journals Biometric Iris Recognition System using Multiscale Feature Extraction Method

2020 ◽  
Vol 8 (6) ◽  
pp. 2298-2303
Keyword(s):  
Iris Recognition ◽  
Computation Time ◽  
Principal Component ◽  
Recognition System ◽  
Extraction Process ◽  
Discrete Wavelet ◽  
Biometric Recognition ◽  
Feature Extraction Method ◽  
Research And Education ◽  
Hand Geometry

By an growing demand for security systems, identification of individuals based on biometric techniques has been a major role of research and education. Biometric recognition examines unique behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry etc. The iris is one of the highly consistentmethods that used to identify individuals because it is fixed and does not change throughout life. This features have led to increasing importance in its use for biometric recognition. In this study, we proposed a system combiningDiscrete Wavelet Transformation and Principal Component Analysis forfeature extraction process of an iris. The idea of using DWT behind PCA is to decrease the resolution of the iris pattern. The Discrete Wavelet Transform (DWT) is depend on sub-band codingwhichreduces the computation time and resources required. PCA is used for further extraction. Our experimental calculation supports the efficient performance of the proposed system.

scholarly journals A fast Iris recognition system through optimum feature extraction

2018 ◽  
Author(s):  
Humayan Kabir Rana ◽  
Md. Shafiul Azam ◽  
Mst. Rashida Akhtar ◽  
Julian Quinn ◽  
Mohammad Ali Moni
Keyword(s):  
Feature Extraction ◽  
Iris Recognition ◽  
Principal Component ◽  
Recognition System ◽  
Discrete Wavelet ◽  
Automated Identification ◽  
Biometric Recognition ◽  
Efficient Performance ◽  
Increasing Demand ◽  
Hand Geometry

With an increasing demand for stringent security systems, automated identification of individuals based on biometric methods has been a major focus of research and development over the last decade. Biometric recognition analyses unique physiological traits or behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry, keystrokes or gait. The iris has a complex and unique structure that remains stable over a person's lifetime, features that have led to its increasing interest in its use for biometric recognition. In this study, we proposed a technique incorporating Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for the extraction of the optimum features of an iris and reducing the runtime needed for iris templates classification. The idea of using DWT behind PCA is to reduce the resolution of the iris template. DWT converts an iris image into four frequency sub-bands. One frequency sub-band instead of four has been used for further feature extraction by using PCA. Our experimental evaluation demonstrates the efficient performance of the proposed technique.

scholarly journals A fast iris recognition system through optimum feature extraction

2019 ◽  
Author(s):  
Humayan Kabir Rana ◽  
Md. Shafiul Azam ◽  
Mst. Rashida Akhtar ◽  
Julian M.W. Quinn ◽  
Mohammad Ali Moni
Keyword(s):  
Feature Extraction ◽  
Iris Recognition ◽  
Principal Component ◽  
Recognition System ◽  
Discrete Wavelet ◽  
Automated Identification ◽  
Biometric Recognition ◽  
Efficient Performance ◽  
Increasing Demand ◽  
Hand Geometry

With an increasing demand for stringent security systems, automated identification of individuals based on biometric methods has been a major focus of research and development over the last decade. Biometric recognition analyses unique physiological traits or behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry, keystrokes or gait. The iris has a complex and unique structure that remains stable over a person's lifetime, features that have led to its increasing interest in its use for biometric recognition. In this study, we proposed a technique incorporating Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for the extraction of the optimum features of an iris and reducing the runtime needed for iris templates classification. The idea of using DWT behind PCA is to reduce the resolution of the iris template. DWT converts an iris image into four frequency sub-bands. One frequency sub-band instead of four has been used for further feature extraction by using PCA. Our experimental evaluation demonstrates the efficient performance of the proposed technique.

scholarly journals A fast iris recognition system through optimum feature extraction

2019 ◽  
Author(s):  
Humayan Kabir Rana ◽  
Md. Shafiul Azam ◽  
Mst. Rashida Akhtar ◽  
Julian M.W. Quinn ◽  
Mohammad Ali Moni
Keyword(s):  
Feature Extraction ◽  
Iris Recognition ◽  
Principal Component ◽  
Recognition System ◽  
Discrete Wavelet ◽  
Automated Identification ◽  
Biometric Recognition ◽  
Efficient Performance ◽  
Increasing Demand ◽  
Hand Geometry

With an increasing demand for stringent security systems, automated identification of individuals based on biometric methods has been a major focus of research and development over the last decade. Biometric recognition analyses unique physiological traits or behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry, keystrokes or gait. The iris has a complex and unique structure that remains stable over a person's lifetime, features that have led to its increasing interest in its use for biometric recognition. In this study, we proposed a technique incorporating Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for the extraction of the optimum features of an iris and reducing the runtime needed for iris templates classification. The idea of using DWT behind PCA is to reduce the resolution of the iris template. DWT converts an iris image into four frequency sub-bands. One frequency sub-band instead of four has been used for further feature extraction by using PCA. Our experimental evaluation demonstrates the efficient performance of the proposed technique.

scholarly journals Iris Biometric Modality: A Review

2017 ◽  
Vol 10 (2) ◽  
pp. 502-506 ◽  
Author(s):  
Iqra Mattoo ◽  
Parul Agarwal
Keyword(s):  
Face Recognition ◽  
Iris Recognition ◽  
Review Paper ◽  
Voice Recognition ◽  
Recognition System ◽  
Physiological Traits ◽  
Robust Method ◽  
Patient Identification ◽  
Biometric Recognition ◽  
Hand Geometry

Biometric Recognition is the most suitable and informed identification method which is used in different fields due to its uniqueness of the countless behavioural and physiological traits like hand geometry, finger prints, iris recognition, face recognition, handwriting, voice recognition, etc. Iris recognition system is widely being used as it has inherently distinctive patterns that provide a robust method for the identification purpose. Different nations have already started to use biometric recognition system for the identification purposes including patient identification, border security, etc. In this review paper, different steps that are involved in Iris Recognition system are defined and evaluation of different Iris Recognition methods used by different researchers for each recognition step is done as well.

scholarly journals A fast iris recognition system through optimum feature extraction

2019 ◽  
Vol 5 ◽  
pp. e184 ◽  
Author(s):  
Humayan Kabir Rana ◽  
Md. Shafiul Azam ◽  
Mst. Rashida Akhtar ◽  
Julian M.W. Quinn ◽  
Mohammad Ali Moni
Keyword(s):  
Feature Extraction ◽  
Iris Recognition ◽  
Principal Component ◽  
Recognition System ◽  
Discrete Wavelet ◽  
Automated Identification ◽  
Biometric Recognition ◽  
Efficient Performance ◽  
Increasing Demand ◽  
Hand Geometry

With an increasing demand for stringent security systems, automated identification of individuals based on biometric methods has been a major focus of research and development over the last decade. Biometric recognition analyses unique physiological traits or behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry, keystrokes or gait. The iris has a complex and unique structure that remains stable over a person’s lifetime, features that have led to its increasing interest in its use for biometric recognition. In this study, we proposed a technique incorporating Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for the extraction of the optimum features of an iris and reducing the runtime needed for iris template classification. The idea of using DWT behind PCA is to reduce the resolution of the iris template. DWT converts an iris image into four frequency sub-bands. One frequency sub-band instead of four has been used for further feature extraction by using PCA. Our experimental evaluation demonstrates the efficient performance of the proposed technique.

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