Iris recognition using minimum average correlation energy and principal component

2015 ◽  
Author(s):  
Volnei da S. Klehm ◽  
Wheidima C. Melo ◽  
Felipe S. Farias ◽  
Kenny V. Santos ◽  
S. S. Waldir
Keyword(s):  
Iris Recognition ◽  
Correlation Energy ◽  
Principal Component ◽  
Average Correlation

scholarly journals Student Attendance Management System using Principal Component Analysis Method

2020 ◽  
Vol 9 (4S2) ◽  
pp. 9-15
Keyword(s):  
Management System ◽  
Iris Recognition ◽  
Principal Component ◽  
Cost Effective ◽  
Analysis Method ◽  
Academic Institutions ◽  
Student Attendance ◽  
Eigen Values ◽  
Cost Effective Technique ◽  
Day By Day

Attendance taking and maintaining is a tedious job in the academic institutions where the time of class is restricted. The manual attendance i.e., roll call or paper-based signature systems usually consumes more time and error prone and also possibility of recording proxy attendance is more. Attendance is one of the criteria in considering students’ eligibility for attending the external examinations and also for the promotion to the next semester / year, where these kinds of problems may cause severe effect on the academic institutions. As the strength of students in a class is increasing day by day; monitoring, awarding and maintenance of attendance has becoming a challenge for the academic institutions. As a solution, attendance can be recorded using anyone of the existing biometric techniques like fingerprinting, iris recognition, signature, face recognition etc. Face identification is the best method among all the earlier mentioned methods for implementing in the academic institutions as it does not require human intervention and it is a cost-effective technique. A novel student attendance recording and management system using a MATLAB application, LabVIEW, Camera interface and GSM is proposed in this paper. Students’ faces will be captured with the help of a camera connected to a computer and Eigen values of the captured images will be detected with the help of MATLAB executed by LabVIEW Mathscript node. LabVIEW, a graphical programming environment is adopted for acquiring face, processing and authenticating the student once the match is found. Authenticated student attendance will be updated, and a message will be sent with the help of GSM module interface to myRIO. Proposed system replaces the manual attendance system which improves the performance of existing system.

Target Recognition Technology Based on Minimum Average Correlation Energy Filters

2010 ◽  
Vol 47 (6) ◽  
pp. 061002
Author(s):  
贾欢欢 Jia Huanhuan ◽  
杨璐 Yang Lu ◽  
王文生 Wang Wensheng
Keyword(s):  
Correlation Energy ◽  
Target Recognition ◽  
Average Correlation

IDENTIFICATION OF VAGINA AND PELVIS FROM IRIS REGION USING ARTIFICIAL NEURAL NETWORK

2015 ◽  
Vol 76 (7) ◽  
Author(s):  
Nor’aini A.J. ◽  
Syahrul Akram Z. A. ◽  
Azilah S.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Iris Recognition ◽  
Principal Component ◽  
Body Part ◽  
Medical Application ◽  
The Body ◽  
Correct Identification ◽  
Feed Forward Neural Network ◽  
Artificial Neural

Iris recognition not only can be used in biometrics technology but also in medical application by identifying the region that relates to the body part.  This paper describes a technique for identification of vagina and pelvis regions from iris region using Artificial Neural Network (ANN) based on iridology chart whereby the ANN process utilized Feed Forward Neural Network (FFNN).  The localization of the iris is carried out using two methods namely Circular Boundary Detector (CBD) and Circular Hough Transform (CHT). The iris is segmented based on the iridology chart and unwrapped into polar form using Daugman’s Rubber Sheet Model.  The vagina and pelvis regions are cropped into pixel size of 40x7 for feature extraction using Principal component Analysis (PCA) and classified using FFNN.  In the experiments, 15 pelvis and 20 vagina regions are used for classification. The best result obtained gives overall correct identification from localization using CBD and CHT of about 67% and 81% respectively.  From the experiments, it is observed that vagina and pelvis regions are able to be identified even though the results obtained are not 100% accurate. 

Circular-harmonic function, minimum average correlation energy filters

1991 ◽  
Vol 30 (35) ◽  
pp. 5169 ◽  
Author(s):  
David Casasent ◽  
Anand Iyer ◽  
Gopalan Ravichandran
Keyword(s):  
Harmonic Function ◽  
Correlation Energy ◽  
Average Correlation

scholarly journals Reduction of Multidimensional Image Characteristics Based on Improved KICA

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Jia Dongyao ◽  
Ai Yanke ◽  
Zou Shengxiong
Keyword(s):  
Independent Component Analysis ◽  
Evaluation Method ◽  
Principal Component ◽  
Characteristic Parameter ◽  
Component Analysis ◽  
Independent Component ◽  
Average Correlation ◽  
Analysis Algorithm ◽  
Correlation Degree ◽  
Kernel Independent Component Analysis

The domestic and overseas studies of redundant multifeatures and noise in dimension reduction are insufficient, and the efficiency and accuracy are low. Dimensionality reduction and optimization of characteristic parameter model based on improved kernel independent component analysis are proposed in this paper; the independent primitives are obtained by KICA (kernel independent component analysis) algorithm to construct an independent group subspace, while using 2DPCA (2D principal component analysis) algorithm to complete the second order related to data and further reduce the dimension in the above method. Meanwhile, the optimization effect evaluation method based on Amari error and average correlation degree is presented in this paper. Comparative simulation experiments show that the Amari error is less than 6%, the average correlation degree is stable at 97% or more, and the parameter optimization method can effectively reduce the dimension of multidimensional characteristic parameters.

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 Performances of proposed normalization algorithm for iris recognition

2020 ◽  
Vol 6 (2) ◽  
pp. 161 ◽  
Author(s):  
Yessi Jusman ◽  
Siew Cheok Ng ◽  
Khairunnisa Hasikin
Keyword(s):  
Iris Recognition ◽  
Correlation Energy ◽  
Texture Feature ◽  
Region Of Interest ◽  
Classification Performance ◽  
Recognition Algorithm ◽  
Small Constant ◽  
Hough Transforms ◽  
Effective System ◽  
High Recognition Accuracy

Iris recognition has very high recognition accuracy in comparison with many other biometric features. The iris pattern is not the same even right and left eye of the same person. It is different and unique. This paper proposes an algorithm to recognize people based on iris images. The algorithm consists of three stages. In the first stage, the segmentation process is using circular Hough transforms to find the region of interest (ROI) of given eye images. After that, a proposed normalization algorithm is to generate the polar images than to enhance the polar images using a modified Daugman’s Rubber sheet model. The last step of the proposed algorithm is to divide the enhance the polar image to be 16 divisions of the iris region. The normalized image is 16 small constant dimensions. The Gray-Level Co-occurrence Matrices (GLCM) technique calculates and extracts the normalized image’s texture feature. Here, the features extracted are contrast, correlation, energy, and homogeneity of the iris. In the last stage, a classification technique, discriminant analysis (DA), is employed for analysis of the proposed normalization algorithm. We have compared the proposed normalization algorithm to the other nine normalization algorithms. The DA technique produces an excellent classification performance with 100% accuracy. We also compare our results with previous results and find out that the proposed iris recognition algorithm is an effective system to detect and recognize person digitally, thus it can be used for security in the building, airports, and other automation in many applications.

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