Biometric Identification via PCA and ICA Based Pattern Recognition

2007 ◽  
Author(s):  
Zhengmao Ye ◽  
Yongmao Ye ◽  
H. Mohamadian
Keyword(s):  
Pattern Recognition ◽  
Biometric Identification

scholarly journals Matching of Iris Pattern using Image Processing

2019 ◽  
Vol 8 (2S11) ◽  
pp. 21-23
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Mobile Phone ◽  
Personal Identification ◽  
The Body ◽  
Biometric Identification ◽  
Non Invasive ◽  
Digital Era ◽  
Invasive Method ◽  
Iris Pattern Recognition

Personal identification is very vital in this digital era for simpler mobile phone unlocking to criminal identification in the scene of crime. There are various methods of personal identification ranging from non-invasive methods of presence of moles in the visible parts of the body to the invasive DNA karyotyping. Other in the spectrum being fingerprinting, lip print, foot print, tongue print, palate print etc. As age advances there might be slight variations in finger print, ear biometric etc, where as in iris the amount of pigmentation might vary but the pattern remains almost same from birth to death, unless otherwise there is any injury to the iris which is very remote. Iris pattern recognition is a non-invasive method of biometric identification. Iris architecture is not only complex but also unique to an individual. In this article a methodology is been proposed to match iris pattern.

TOWARDS A FAST METHOD FOR IRIS IDENTIFICATION WITH FRACTAL AND CHAOS GAME THEORY

2012 ◽  
Vol 26 (04) ◽  
pp. 1256011 ◽  
Author(s):  
MAHDI JAMPOUR ◽  
REZA EBRAHIMZADEH ◽  
MAHDI YAGHOOBI ◽  
ADEL SOLEIMANI-NEZHAD
Keyword(s):  
Game Theory ◽  
Pattern Recognition ◽  
Human Body ◽  
Second Step ◽  
Biometric Identification ◽  
Fast Method ◽  
The Third ◽  
Fractal Properties ◽  
Chaos Game ◽  
Iris Identification

Nowadays many techniques are being used to increase the reliability of human identification systems. Iris is a part of human body that is desirable for biometric identification and has favorable factors. We have focused on the reality that iris is a fractal phenomenon in this paper. During the production of new fractals, some features will be extracted by Chaos Game mechanism. These features are useful and effective in iris identification. There are three steps for iris identification with fractal and Chaos Game Theory. The first step is making a new fractal. The second step includes extracting features during the first step. Finally, the iris identification based on extracted features is the third step. We have named this technique Iris Identification based-Fractal and Chaos Game Theory (Iris-IFCGT). This technique has some fractal properties like stability against zoom, removing part of the iris image, no sensitivity on rotation and so on as well as desirable speed which helps preventing time consuming process of pattern recognition.

scholarly journals Iris segmentation using a new unsupervised neural approach

2020 ◽  
Vol 9 (1) ◽  
pp. 58
Author(s):  
Hicham Ohmaid ◽  
S. Eddarouich ◽  
A. Bourouhou ◽  
M. Timouyas
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Iris Recognition ◽  
Recognition System ◽  
Automatic Recognition ◽  
Biometric Identification ◽  
Iris Segmentation ◽  
Biometric System ◽  
Canny Edge ◽  
Epigenetic Patterns

<span lang="EN-GB">A biometric system of identification and authentication provides automatic recognition of an individual based on certain unique features or characteristic possessed by an individual. Iris recognition is a biometric identification method that uses pattern recognition on the images of the iris. Owing to the unique epigenetic patterns of the iris, Iris recognition is considered as one of the most accurate methods in the field of biometric identification. One of the crucial steps in the iris recognition system is the iris segmentation because it significantly affects the accuracy of the feature extraction the iris. The segmentation algorithm proposed in this article starts with determining the regions of the eye using unsupervised neural approach, after the outline of the eye is found using the Canny edge, The Hough Transform is employed to determine the </span><span lang="EN-US">center</span><span lang="EN-GB"> and radius of the pupil and the iris.</span>

scholarly journals Biometric Identification of Taxodium spp. and Their Hybrid Progenies by Electrochemical Fingerprints

Biosensors ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 403
Author(s):  
Yuhong Zheng ◽  
Da Wang ◽  
Xiaolong Li ◽  
Ziyang Wang ◽  
Qingwei Zhou ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Phylogenetic Study ◽  
Biometric Identification ◽  
Hybrid Progeny ◽  
Plant Identification ◽  
Electrochemically Active ◽  
Leaf Tissues ◽  
Plant Hybrid ◽  
The Relationship ◽  
Active Substances

The use of electrochemical fingerprints for plant identification is an emerging application in biosensors. In this work, Taxodium ascendens, T. distichum, T. mucronatum, and 18 of their hybrid progenies were collected for this purpose. This is the first attempt to use electrochemical fingerprinting for the identification of plant hybrid progeny. Electrochemical fingerprinting in the leaves of Taxodium spp. was recorded under two conditions. The results showed that the electrochemical fingerprints of each species and progeny possessed very suitable reproducibility. These electrochemical fingerprints represent the electrochemical behavior of electrochemically active substances in leaf tissues under specific conditions. Since these species and progenies are very closely related to each other, it is challenging to identify them directly using a particular electrochemical fingerprinting. Therefore, electrochemical fingerprints measured under different conditions were used to perform pattern recognition. We can identify different species and progenies by locating the features in different pattern maps. We also performed a phylogenetic study with data from electrochemical fingerprinting. The results proved that the electrochemical classification results and the relationship between them are closely related.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Bayesian removal of noise for increased sensitivity in vector pattern recognition lattice imaging of interfaces

1993 ◽  
Vol 51 ◽  
pp. 994-995
Author(s):  
L. Fei ◽  
P. Fraundorf
Keyword(s):  
Pattern Recognition ◽  
Perfect Crystal ◽  
Unit Cell ◽  
Ion Milling ◽  
Beam Radiation ◽  
Major Interest ◽  
Unit Cells ◽  
Mapping Process ◽  
Increased Sensitivity ◽  
Electron Microscopes

Interface structure is of major interest in microscopy. With high resolution transmission electron microscopes (TEMs) and scanning probe microscopes, it is possible to reveal structure of interfaces in unit cells, in some cases with atomic resolution. A. Ourmazd et al. proposed quantifying such observations by using vector pattern recognition to map chemical composition changes across the interface in TEM images with unit cell resolution. The sensitivity of the mapping process, however, is limited by the repeatability of unit cell images of perfect crystal, and hence by the amount of delocalized noise, e.g. due to ion milling or beam radiation damage. Bayesian removal of noise, based on statistical inference, can be used to reduce the amount of non-periodic noise in images after acquisition. The basic principle of Bayesian phase-model background subtraction, according to our previous study, is that the optimum (rms error minimizing strategy) Fourier phases of the noise can be obtained provided the amplitudes of the noise is given, while the noise amplitude can often be estimated from the image itself.

Pattern Recognition: The Basis of All Cognition?

1989 ◽  
Vol 34 (11) ◽  
pp. 988-989
Author(s):  
Erwin M. Segal
Keyword(s):  
Pattern Recognition
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