Fusion of Fingerprint Recognition Methods for Robust Human Identification

2008 ◽  
Author(s):  
Fernanda Pereira Sartori Falguera ◽  
Aparecido Nilceu Marana ◽  
Juan Rogelio Falguera
Keyword(s):  
Human Identification ◽  
Fingerprint Recognition

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 A Review on Detection and Analysis of Fingerprint

2020 ◽  
pp. 51-56
Author(s):  
Bhavani Ranbida ◽  
Chandra Sekhar Panda
Keyword(s):  
Main Idea ◽  
Human Identification ◽  
Recognition System ◽  
Research Paper ◽  
Fingerprint Recognition ◽  
Fingerprint Analysis ◽  
Human Recognition ◽  
Finger Print ◽  
Image Format ◽  
Fingerprint Detection

Fingerprint analysis is the most essential part of human identification or human recognition. At present too many biometric techniques are presented for fingerprint identification and fingerprint recognition. We know that, a fingerprint contains a lot of key point like Y shape, delta, ridge ending, ridge staring, minutiae’s pattern and etc. All points are apodictic of unique for any human fingerprint. The aim of this paper is to review numerous recently work on fingerprint recognition system. Fingerprint detection is a very important topic to identify the correct person’s finger print and can make everything secure. The main idea of this research paper is to find out the 100% correct fingerprint details from any document (in image format). In this research paper we will get that how to identify the details of fingerprint from image.

Analysis of Fingerprint Recognition System Using Neural Network

2020 ◽  
Author(s):  
Ganesh Awasthi ◽  
Dr. Hanumant Fadewar ◽  
Almas Siddiqui ◽  
Bharatratna P. Gaikwad
Keyword(s):  
Neural Network ◽  
Recognition System ◽  
Fingerprint Recognition

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.

scholarly journals Human Identification Using Contrast Enhanced Segmented Palm-Vein Images

2020 ◽  
Vol 993 ◽  
pp. 012097
Author(s):  
K Nandini ◽  
T. Surendran ◽  
S. Sobana ◽  
B. K. Chitra ◽  
T. Kalaiselvi
Keyword(s):  
Human Identification ◽  
Contrast Enhanced ◽  
Palm Vein
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