Fusion of hand-shape and palm-print traits using morphology for bi-modal biometric authentication

2018 ◽  
Vol 10 (4) ◽  
pp. 368 ◽  
Author(s):  
Wen Shiung Chen ◽  
Wei Chang Wang
Keyword(s):  
Biometric Authentication ◽  
Hand Shape ◽  
Palm Print

Fusion of hand-shape and palm-print traits using morphology for bi-modal biometric authentication

2018 ◽  
Vol 10 (4) ◽  
pp. 368
Author(s):  
Wen Shiung Chen ◽  
Wei Chang Wang
Keyword(s):  
Biometric Authentication ◽  
Hand Shape ◽  
Palm Print

scholarly journals Efficient Image Retrieval Techniques for Multi Biometric Images to Improve Biometric Authentication

2020 ◽  
Vol 8 (5) ◽  
pp. 443-446
Keyword(s):  
Error Rate ◽  
Computation Time ◽  
Home Automation ◽  
Biometric Authentication ◽  
Retrieval Time ◽  
Retrieval Accuracy ◽  
Maintenance System ◽  
Behavioral Features ◽  
Physical Traits ◽  
Palm Print

Multi model biometrics security is focused on recognizing human depends on non identical physical and behavioral features. In physical, recognition of the human features like iris, face, finger-prints and palm print are normally used to improve the security and proper authentication. Recent days, biometrics based identification is used in home automation, bank locker system; attendance maintenance system, crime branch etc.. to identify the human for providing secured services. The proposed multi model biometrics system recognizes the human based on physical traits like iris, finger- print, faces and palm- print to obtain better retrieval accuracy, precision and retrieval speed and reduced error rate, retrieval time and computation time.

PALM PRINTS 'LINES DO TELL'- AN INNOVATING PARAPHERNALIA AS BIOMETRIC AUTHENTICATION

2021 ◽  
pp. 37-38
Author(s):  
Sameera Shamim Khan ◽  
Smitha Naik ◽  
Arshad Khan
Keyword(s):  
High Resolution ◽  
Software Systems ◽  
Biometric Authentication ◽  
Low Resolution ◽  
Classi Cation ◽  
Cation System ◽  
High Resolution Images ◽  
Palm Print ◽  
Commercial Applications ◽  
Low Resolution Images

Authentication in personal identication using palm print method provides valuable evidence in one's identication. It has been investigated over years by different methods employed by both high resolution images which are further processed by different computerized techniques and software systems and low resolution images which have attracted many researchers attention. This paper proposes a brief introduction about palm prints its different methods employed and the current classication system which is less time consuming followed for research to be carried out for biometric authentication and scientic evidences which is useful for civil and commercial applications.

Handwritten Signatures Verification Through Their Acoustic Patterns Based on the Discrete Wavelet-Packet Transform and Semantic-Matching Classifiers

2016 ◽  
Vol 10 (04) ◽  
pp. 557-567 ◽  
Author(s):  
Daniel Angelotti Armiato ◽  
Yuzo Yano ◽  
Vinícius Zani de Faveri ◽  
Rodrigo Capobianco Guido
Keyword(s):  
State Of The Art ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Discrete Wavelet ◽  
Semantic Matching ◽  
Biometric Authentication ◽  
Discrete Wavelet Packet Transform ◽  
Time Frequency ◽  
Non Invasive ◽  
Hand Shape

Biometric authentication based on fingerprints, voice, hand shape, facial measurements and iris analysis, among others, are quite common nowadays. In a similar manner, the analysis of acoustic patterns generated during the friction between pen and paper at the time a person subscribes has been shown to be a feasible, adequate, and non-invasive alternative to those techniques. An interesting implementation for such an approach, which is described in this paper, is based on the association of the time-frequency analysis supported by the discrete wavelet-packet transform with one of two pattern-matching classifiers, namely Euclidian norma and an original scoring equation derived from correlation, acting semantically. Valuable results were obtained during the tests, motivating further research. The proposed technique is novel on literature, offering a contribution to the state-of-the-art.

Template Protection Using Multi Biometric Web Modulo Graph

2019 ◽  
Vol 16 (11) ◽  
pp. 4883-4888
Author(s):  
P. Kumaran ◽  
R. Ashoka Rajan ◽  
T. Veeramani ◽  
R. Thilagavathy
Keyword(s):  
Graph Structure ◽  
Biometric Authentication ◽  
Equal Error Rate ◽  
Feature Vectors ◽  
Correct Sequence ◽  
Template Protection ◽  
Novel Method ◽  
Authentication System ◽  
Palm Print ◽  
Enrollment Process

To develop a complete biometric authentication system, security is highly needed. Even though there are several methods for storing fingerprint templates, they are compromised by the attacker leaving it as an unprotected system. In this paper, a novel method is proposed for protecting biometrics through an user defined graph named Web Modulo Graph. Feature vectors are extracted from the Left Fingerprint, Right Fingerprint and Palm Print during the enrollment process. The captured information from the biometrics are combined and stored in Web Modulo Graph where the insertion and traversal of feature vectors are unknown to the attacker. So even if the database or the graph structure is stolen by the attacker the correct sequence cannot be obtained. In this case, guessing the correct sequence is not almost possible as user defined graph is used and the system can achieve this with an Equal Error Rate (EER) of 4.8%. After various analyses, the proposed system is found to have high computational hardness.

Palm Print and Palm Vein Biometric Authentication System

2017 ◽  
pp. 539-545 ◽  
Author(s):  
J. Ajay Siddharth ◽  
A. P. Hari Prabha ◽  
T. J. Srinivasan ◽  
N. Lalithamani
Keyword(s):  
Biometric Authentication ◽  
Authentication System ◽  
Palm Print ◽  
Palm Vein

Multimodal Biometric Authentication Algorithm Using Iris, Palm Print, Face and Signature with Encoded DWT

2017 ◽  
Vol 99 (1) ◽  
pp. 23-34 ◽  
Author(s):  
E. Sujatha ◽  
A. Chilambuchelvan
Keyword(s):  
Biometric Authentication ◽  
Palm Print
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