scholarly journals A Novel Approach for Diagnosis of Diabetes Using Iris Image Processing Technique and Evaluation Parameters

2021 ◽  
Author(s):  
Shilpa Jagtap ◽  
J L Mudegaonkar ◽  
Sanjay Patil ◽  
Dinesh Bhoyar
Keyword(s):  
Wavelet Transforms ◽  
Template Matching ◽  
Hamming Distance ◽  
Gabor Filter ◽  
Rejection Rate ◽  
Image Processing Technique ◽  
Computational Time ◽  
Support Vector ◽  
Adaptive Boosting ◽  
Human Iris

This paper presented here deals with study of identification and verification approach of Diabetes based on human iris pattern. In the pre-processing of this work, region of interest according to color (ROI) concept is used for iris localization, Dougman's rubber sheet model is used for normalization and Circular Hough Transform can be used for pupil and boundary detection. To extract features, Gabor Filter, Histogram of Oriented Gradients, five level decomposition of wavelet transforms likeHaar, db2, db4, bior 2.2, bior6.8 waveletscan be used. Binary coding scheme binaries’ the feature vector coefficients and classifier like hamming distance, Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), Neural Networks (NN), Random Forest (RF) and Linear Discriminative Analysis (LDA) with shrinkage parametercan be used for template matching. Performance parameters such as Computational time, Hamming distance variation, False Acceptance Rate (FAR), False Rejection Rate (FRR), Accuracy, and Match ratio can be calculated for the comparison purpose.

Fault isolation of analog circuit using an optimized ensemble empirical mode decomposition approach based on multi-objective optimization

2021 ◽  
pp. 095965182110205
Author(s):  
Alireza Moezi ◽  
Seyed Mohamad Kargar
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Wavelet Transforms ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Computational Time ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Decomposition Approach ◽  
Mode Decomposition

This article proposed a practical approach to isolating faults in analog circuits. The contribution of this article is twofold. First, the optimized empirical mode decomposition approach is presented based on the Hellinger distance such that there is a minimum dependency between intrinsic mode functions. Features with high distinction could be extracted by employing intrinsic mode functions in fault detection problem of analog benchmark circuits. Second, the non-dominated sorting genetic algorithm is employed to retain excellent features and speed up the execution, resulting in the high accuracy of fault detection and isolation. The number of features and mean squared error are selected as objective functions. The features from the data are also extracted using the fast Fourier and wavelet transforms for comparison. Finally, the support vector machine and artificial neural network are employed to isolate faults. Two circuits under test are simulated, and the output signals of the faulty and fault-free circuits are extracted by the Monte Carlo analysis. According to the obtained simulation results, the proposed method with a low-dimensional feature vector outperformed the previous methods, and the computational time has also reduced significantly.

scholarly journals Enhanced Biometric Recognition for Secure Authentication using Iris Preprocessing and Hyper Elliptic Curve Cryptography

2020 ◽  
Author(s):  
Vani Rajasekar ◽  
Premalatha J ◽  
Sathya K
Keyword(s):  
Elliptic Curve ◽  
Elliptic Curve Cryptography ◽  
Iris Recognition ◽  
Gabor Filter ◽  
Rejection Rate ◽  
Acceptance Rate ◽  
Computational Time ◽  
Recognition Time ◽  
Intentional Attack ◽  
Hyper Elliptic Curve

Abstract Biometrics combined with cryptography can be employed to solve the conceptual and factual identity frauds in digital authentication. Biometric traits are proven to provide enhanced security for detecting crimes because of its interesting features such as accuracy, stability and uniqueness. Although diverse techniques have been raised to address this objective, limitations such as higher computational time, minimal accuracy and maximum recognition time remain. To overcome these challenges an enhanced iris recognition approach has been proposed based on Hyper Elliptic Curve Cryptography (HECC). The proposed study uses 2D Gabor filter approach for perfect feature extraction in iris preprocessing. Light weight cryptographic scheme called HECC was employed to encrypt the iris template to avoid intentional attack by the intruders. The benchmark CASIA Iris V-4 and IITD Iris datasets were used in the proposed approach for experimental analysis. The result analysis witnessed that the prime objective of the research such as lesser false acceptance rate, lesser false rejection rate, maximum accuracy of 99.74%, maximum true acceptance rate of 100%, and minimal recognition time of 3 seconds has been achieved. Also it has been identified that the proposed study outperforms other existing well known techniques.

scholarly journals Enhanced Biometric Recognition for Secure Authentication Using Iris Preprocessing and Hyperelliptic Curve Cryptography

2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Vani Rajasekar ◽  
J. Premalatha ◽  
K. Sathya
Keyword(s):  
Iris Recognition ◽  
Hyperelliptic Curve ◽  
Gabor Filter ◽  
Rejection Rate ◽  
Acceptance Rate ◽  
Computational Time ◽  
Recognition Time ◽  
Intentional Attack ◽  
Result Analysis ◽  
Hyperelliptic Curve Cryptography

Biometrics combined with cryptography can be employed to solve the conceptual and factual identity frauds in digital authentication. Biometric traits are proven to provide enhanced security for detecting crimes because of its interesting features such as accuracy, stability, and uniqueness. Although diverse techniques have been raised to address this objective, limitations such as higher computational time, minimal accuracy, and maximum recognition time remain. To overcome these challenges, an enhanced iris recognition approach has been proposed based on hyperelliptic curve cryptography (HECC). The proposed study uses the 2D Gabor filter approach for perfect feature extraction in iris preprocessing. A lightweight cryptographic scheme called HECC was employed to encrypt the iris template to avoid intentional attack by the intruders. The benchmark CASIA Iris V-4 and IITD iris datasets were used in the proposed approach for experimental analysis. The result analysis witnessed that the prime objective of the research such as lesser false acceptance rate, lesser false rejection rate, maximum accuracy of 99.74%, maximum true acceptance rate of 100%, and minimal recognition time of 3 seconds has been achieved. Also, it has been identified that the proposed study outperforms other existing well-known techniques.

scholarly journals A Fast and Efficient Palmprint Identification Method for a Large Database

1970 ◽  
Vol 5 (2) ◽  
pp. 118-126 ◽  
Author(s):  
Mongkon Sakdanupab ◽  
Nongluk Covavisaruch
Keyword(s):  
Template Matching ◽  
Hamming Distance ◽  
Gabor Filter ◽  
Experimental Results ◽  
Large Database ◽  
Identification Methods ◽  
Palmprint Identification ◽  
Identification Method ◽  
Head Line ◽  
Log Gabor

This paper proposes a fast and efficient palmprint identification method for a large database. The process is accelerated as a result of our efficient palmprint classification and matching scheme. Palmprint classification method is based on principle lines which are life line, head line and heart line. Palmprints’ features are extracted with Log-Gabor filter and matched with Hamming distance in the most potential palmprint group/category, and if necessary, continues orderly to the less potential ones. Experiments are done with 2 hand databases, Visgraph database and CU-CGCI hand database. Experimental results show that the proposed method can greatly reduce the number of template matching from 100% (as in general identification methods) to 33.2-38.2% while maintaining the equivalent EER as the general identification method.

Klasifikasi Citra Daun dengan Metode Gabor Co-Occurence

2016 ◽  
Vol 7 (2) ◽  
pp. 39-47
Author(s):  
Mutmainnah Muchtar ◽  
Laili Cahyani
Keyword(s):  
Cross Validation ◽  
Unique Feature ◽  
Gabor Filter ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Support Vector ◽  
Filter Methods ◽  
Average Accuracy ◽  
Crucial Part ◽  
Fold Cross Validation

Plant takes a crucial part in mankind existences. The development of digital image processing technique made the plant classification task become a lot of easier. Leaf is a part of plant that can be used for plant classification where texture of the leaf is a common feature that been used for classification process. Texture offers a unique feature and able to work even when the leaf is damaged or overly big in size which sometimes made the acquisition process become more difficult. This study offers a combination of Gabor filter methods and co-occurrence matrices to produce the most representative features for leaf classification. Classification using SVM with 5-fold cross validation system shows that the proposed Gabor Co-Occurence methods was able to reach average accuracy up to 89.83%. Terms: Leaf, Gabor Co-occurence, Support Vector Machine, Texture

scholarly journals Authentication Based on Texture Analysis And SVM Classification

2011 ◽  
pp. 61-66
Author(s):  
Sanjay Kumar Mohanty ◽  
Prasant Kumar Pattnaik
Keyword(s):  
Feature Vector ◽  
Hamming Distance ◽  
Gabor Filter ◽  
Gabor Wavelet ◽  
Support Vector ◽  
Wavelet Filter ◽  
Canny Edge Detection ◽  
Circular Hough Transform ◽  
Canny Edge ◽  
Log Gabor

This paper presents for identification and here used a fusion mechanism that amalgamates both, a Canny Edge detection and a Circular Hough Transform to detect the iris boundaries in the eye’s digital image. We then applied the Gabor Wavelet filter instead of using 1D Log-Gabor filter in order to exact the deterministic patterns in a person’s iris in the form of a feature vector. By comparing the quantized vectors using the Hamming Distance operator, we determine finally and for classification used Support vector Machine.

Compact Wake-Up Word Speech Recognition on Embedded Platforms

2014 ◽  
Vol 596 ◽  
pp. 402-405 ◽  
Author(s):  
An Hao Xing ◽  
Ta Li ◽  
Jie Lin Pan ◽  
Yong Hong Yan
Keyword(s):  
Speech Recognition ◽  
Template Matching ◽  
Rejection Rate ◽  
Recognition System ◽  
Support Vector ◽  
New Paradigm ◽  
Scoring Method ◽  
Resource Cost ◽  
Speech Feature ◽  
Embedded Platforms

The wake-up word speech recognition system is a new paradigm in the field of automatic speech recognition (ASR). This new paradigm is not yet widely recognized but useful in many applications such as mobile phones and smart home systems. In this paper we describe the development of a compact wake-up word recognizer for embedded platforms. To keep resource cost low, a variety of simplification techniques are used. Speech feature observations are compressed to lower dimension and the simple distance-based template matching method is used in place of complex Viterbi scoring. We apply double scoring method to achieve a better performance. To cooperate with double scoring method, the support vector machine classifier is used as well. We were able to accomplish a performance improvement with false rejection rate reduced from 6.88% to 5.50% and false acceptance rate reduced from 8.40% to 3.01%.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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