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Chithra Devi, M.
- Comparative Study on Biometric Iris Recognition based on Hamming Distance and Multi Block Local Binary Pattern
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Authors
Affiliations
1 Department of Computer Science, A.V.V.M. Sri Pushpam College, Poondi-613503, IN
2 Department of Software Engineering, Periyar Maniammai University, Vallam-613403, IN
1 Department of Computer Science, A.V.V.M. Sri Pushpam College, Poondi-613503, IN
2 Department of Software Engineering, Periyar Maniammai University, Vallam-613403, IN
Source
Indian Journal of Science and Technology, Vol 8, No 11 (2015), Pagination:Abstract
Personal identification based on biometric is most essential to ensure security. Recognition based on iris unique texture is a reliable, simple and fast. The abundant as well as unique patterns of iris are extracted. Matrix format template is generated that contains 4800 elements for each iris. Multi block local binary pattern, hamming distance and support vector machine performs matching based on the template’s unique features of iris. The experimental results of this proposed work illustrate a better performance. The popular CASIA (Chinese Academy of Sciences – Institute of Automation) iris database with hundred users’ eye image samples are experimented to prove, that the multi block local binary pattern is comparatively better with minimal true rejection rate.Keywords
Hamming Distance, Iris Preprocessing, Iris Template, Multi Block Local Binary PatternFull Text
- Pair of Iris Recognition Using Feedforward Neural Networks
Abstract Views :142 |
PDF Views:3
Authors
Affiliations
1 Department of Software Engineering, Periyar Maniammai University, Thanjavur, Tamil Nadu, IN
2 Department of Computer Science Engineering, Periyar Maniammai University, Thanjavur, IN
1 Department of Software Engineering, Periyar Maniammai University, Thanjavur, Tamil Nadu, IN
2 Department of Computer Science Engineering, Periyar Maniammai University, Thanjavur, IN
Source
Biometrics and Bioinformatics, Vol 4, No 5 (2012), Pagination: 222-225Abstract
Pair of iris recognition is very effective for person identification due to the iris unique features and the protection of the iris from the environment and aging. In addition it is well suitable to embark upon accidental or ophthalmological disease issue. This paper presents a simple methodology for pre-processing pair of iris images which means both left and right eye of human(instead of either right or left eye) and the design and training of feedforward artificial neural network for iris recognition system. Three different iris image data partitioning techniques and two data coding are proposed and explored. We also experiment with various number of hidden layers, number of neurons in each hidden layer, input format (binary vs. analog) percent of data used for training vs testing, and with the addition of noise. Our recognition system achieves high accuracy despite using simple data preprocessing and a simple neural network.
Keywords
Backpropagation Training Algorithm, Data Partitioning Feedforward Neural Networks, Pair of Iris Recognition, Pre-Processing.
- IRIS Recognition Based on Kernels of Support Vector Machine
Abstract Views :162 |
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Authors
Affiliations
1 Department of Computer Science and Engineering, Ponnaiyah Ramajayam Institute of Science and Technology University, IN
2 Department of Computer Science, A. Veeriya Vandayar Memorial Sri Pushpam College, IN
3 Department of Software Engineering, Periyar Maniammai University, IN
1 Department of Computer Science and Engineering, Ponnaiyah Ramajayam Institute of Science and Technology University, IN
2 Department of Computer Science, A. Veeriya Vandayar Memorial Sri Pushpam College, IN
3 Department of Software Engineering, Periyar Maniammai University, IN
Source
ICTACT Journal on Soft Computing, Vol 5, No 2 (2015), Pagination: 889-895Abstract
Ensuring security biometrically is essential in most of the authentication and identification scenario. Recognition based on iris patterns is a thrust area of research cause to provide reliable, simple and rapid identification system. Machine learning classification algorithm of support vector machine [SVM] is applied in this work for personal identification. The profuse as well as unique patterns of iris are acquired and stored in the form of matrix template which contains 4800 elements for each iris. The row vectors of 2400 elements are passed as inputs to SVM classifier. The SVM generates separate classes for each user and performs matching based on the template's unique spectral features of iris. The experimental results of this proposed work illustrate a better performance of 98.5% compared to the existing methods such as hamming distance, local binary pattern and various kernels of SVM. The popular CASIA (Chinese Academy of Sciences - Institute of Automation) iris database with fifty users' eye image samples are experimented to prove, that the least Square method of Quadratic kernel based SVM is comparatively better with minimal true rejection rate.Keywords
Iris Preprocessing, Iris Template, Quadratic Kernel, Support Vector Machine, Hamming, Local Binary Pattern.- A Comprehensive Review on Diagnosis and Classification of paddy Leaf Diseases Using Advanced Computer Vision Technologies
Abstract Views :114 |
PDF Views:0
Authors
Affiliations
1 PG and Research Department of Computer Science, A.V.V.M. Sri Pushpam College, Affiliated to Bharathidasan University, IN
2 Queens College of Arts and Science for Women, Affiliated to Bharathidasan University, IN
1 PG and Research Department of Computer Science, A.V.V.M. Sri Pushpam College, Affiliated to Bharathidasan University, IN
2 Queens College of Arts and Science for Women, Affiliated to Bharathidasan University, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 4 (2023), Pagination: 2973-2986Abstract
Food is required for human survival. Paddy is a vital food crop serving 60% of the Indian population. Food quality is determined by the plant yield. Unfavorable environmental circumstances, soil fertility, bacteria, viruses, nematodes, fertilizer use, and the absence of nutritional shortages substantially influence plant yield. As a result, it is critical to protect the plants from illness. Crop yield must be improved to meet food scarcity of growing population. Although disease symptoms are apparent in various parts of plant like leaves, stem, fruits and stem, the infections are commonly observed in the leaves. Understanding plant pathology plays a vital role in disease detection. Early detection of diseases is a prompt intervention that aids the farmers in controlling disease spread, resulting in increased agricultural quantity and quality. Image processing techniques with advanced computer vision technologies like machine learning and deep learning have proven the automation of plant disease diagnosis precisely. The main objectives of this research are to investigate computer vision technologies for the early identification of plant diseases and help novice researchers in the same domain learn about plant diseases and the methodologies for disease detection in paddy plant leaves. Consequently this manuscript reviews significant paddy plant infections, highlights related study of tools and techniques, current research, limitations and conclusions for future research in this field.Keywords
Paddy Disease Detection, Machine Learning, Deep Learning, Preprocessing, Segmentation, Feature Extraction, Classification, Convolutional Neural Network.References
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