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).

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.

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 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.

scholarly journals A face recognition software framework based on principal component analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254965
Author(s):  
Peng Peng ◽  
Ivens Portugal ◽  
Paulo Alencar ◽  
Donald Cowan
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Iris Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Software Framework ◽  
Software Developers ◽  
Practical Applications

Face recognition, as one of the major biometrics identification methods, has been applied in different fields involving economics, military, e-commerce, and security. Its touchless identification process and non-compulsory rule to users are irreplaceable by other approaches, such as iris recognition or fingerprint recognition. Among all face recognition techniques, principal component analysis (PCA), proposed in the earliest stage, still attracts researchers because of its property of reducing data dimensionality without losing important information. Nevertheless, establishing a PCA-based face recognition system is still time-consuming, since there are different problems that need to be considered in practical applications, such as illumination, facial expression, or shooting angle. Furthermore, it still costs a lot of effort for software developers to integrate toolkit implementations in applications. This paper provides a software framework for PCA-based face recognition aimed at assisting software developers to customize their applications efficiently. The framework describes the complete process of PCA-based face recognition, and in each step, multiple variations are offered for different requirements. Some of the variations in the same step can work collaboratively and some steps can be omitted in specific situations; thus, the total number of variations exceeds 150. The implementation of all approaches presented in the framework is provided.

scholarly journals Multibiometric authentication with MATLAB simulation

2018 ◽  
Vol 7 (1.7) ◽  
pp. 47
Author(s):  
P Selvarani ◽  
N Malarvizhi
Keyword(s):  
Iris Recognition ◽  
Hamming Distance ◽  
Research Work ◽  
Histogram Equalization ◽  
Fingerprint Recognition ◽  
Morphological Operations ◽  
Matlab Simulation ◽  
Rubber Sheet ◽  
Recognition Process ◽  
Canny Edge

Multimodal Biometric Authentication has been used as more security purpose for establishing the user Identification, Authentication and Verification purpose. Multimodal Biometric like Fingerprint and iris are used in this research work for authentication purpose using Matlab simulation. Fingerprint recognition process like Image Enhancement, binarization, Segmentation, thinning, Minutia marking, and Matching are performed with various techniques like Histogram Equalization, Adaptive Binarization, Morphological operations, Minutiae based techniques etc.,Iris recognition process like Segmentation, Normalization, Encoding and Matching are performed with various techniques like Canny edge detection, Daughman’s Rubber sheet model, Hamming Distance etc., can be applied for Fingerprint and iris recognition for authentication purpose. Finally Performance the measure of Precision, Recall, F-Score and Accuracy has evaluated in both fingerprint and iris. It can be concluded Iris Accuracy is higher 0.96% compared with fingerprint accuracy 0.81%.

A survey of liveness detection methods for face biometric systems

Sensor Review ◽  
2017 ◽  
Vol 37 (3) ◽  
pp. 346-356 ◽  
Author(s):  
Yang Xin ◽  
Yi Liu ◽  
Zhi Liu ◽  
Xuemei Zhu ◽  
Lingshuang Kong ◽  
...  
Keyword(s):  
Face Recognition ◽  
Future Development ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Detection Methods ◽  
Content Type ◽  
Liveness Detection ◽  
Biometric Systems ◽  
The Future ◽  
Face Biometric

Purpose Biometric systems are widely used for face recognition. They have rapidly developed in recent years. Compared with other approaches, such as fingerprint recognition, handwriting verification and retinal and iris scanning, face recognition is more straightforward, user friendly and extensively used. The aforementioned approaches, including face recognition, are vulnerable to malicious attacks by impostors; in such cases, face liveness detection comes in handy to ensure both accuracy and robustness. Liveness is an important feature that reflects physiological signs and differentiates artificial from real biometric traits. This paper aims to provide a simple path for the future development of more robust and accurate liveness detection approaches. Design/methodology/approach This paper discusses about introduction to the face biometric system, liveness detection in face recognition system and comparisons between the different discussed works of existing measures. Originality/value This paper presents an overview, comparison and discussion of proposed face liveness detection methods to provide a reference for the future development of more robust and accurate liveness detection approaches.

Adults with Dyslexia Have a Deficit in Voice Recognition

1994 ◽  
Vol 78 (1) ◽  
pp. 304-306 ◽  
Author(s):  
Gregory W. Z. Brachacki ◽  
Angela J. Fawcett ◽  
Roderick I. Nicolson
Keyword(s):  
Face Recognition ◽  
Voice Recognition ◽  
Ceiling Effect ◽  
Dyslexic Group ◽  
Dyslexic Students

A group of 7 dyslexic students and 8 nondyslexic students matched for age and IQ were tested on recognition of computer-presented voices and faces. Although face recognition showed a ceiling effect which prevented any solid conclusions being drawn from this task, the dyslexic group were significantly impaired on the recognition of voices.

scholarly journals Iris Recognition for Human Identification

2010 ◽  
Author(s):  
Lajari Alandkar ◽  
Sachin Gengaje ◽  
R. B. Patel ◽  
B. P. Singh
Keyword(s):  
Iris Recognition ◽  
Human Identification
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