Iris recognition using radon transform and GLCM

Bhagat, K. S.; Patil, Pramod B.; Deshmukh, Ratnadeep R.; Kirange, D. K.; Waghmare, Swapnil

doi:10.1109/icacci.2017.8126182

Cited by 5 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Several studies used these approaches to segment the iris and retinal blood vessels for effective feature extraction [21][22][23][24][25]. These approaches address the constraints of classic feature extraction methods to per-form authentication without reproducing the images.…”

Section: Related Workmentioning

confidence: 99%

Achieving Information Security by multi-Modal Iris-Retina Biometric Approach Using Improved Mask R-CNN

El-Sayed

Latif

2023

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

The need for reliable user recognition (identification/authentication) techniques has grown in response to heightened security concerns and accelerated advances in networking, communication, and mobility. Biometrics, defined as the science of recognizing an individual based on his or her physical or behavioral characteristics, is gaining recognition as a method for determining an individual's identity. Various commercial, civilian, and forensic applications now use biometric systems to establish identity. The purpose of this paper is to design an efficient multimodal biometric system based on iris and retinal features to assure accurate human recognition and improve the accuracy of recognition using deep learning techniques. Deep learning models were tested using retinographies and iris images acquired from the MESSIDOR and CASIA-IrisV1 databases for the same person. The Iris region was segmented from the image using the custom Mask R-CNN method, and the unique blood vessels were segmented from retinal images of the same person using principal curvature. Then, in order to aid precise recognition, they optimally extract significant information from the segmented images of the iris and retina. The suggested model attained 98% accuracy, 98.1% recall, and 98.1% precision. It has been discovered that using a custom Mask R-CNN approach on Iris-Retina images improves efficiency and accuracy in person recognition.

show abstract

Section: Related Workmentioning

confidence: 99%

Achieving Information Security by multi-Modal Iris-Retina Biometric Approach Using Improved Mask R-CNN

El-Sayed

Latif

2023

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

show abstract

“…Toward adapt the main patterns into the necessary designs, the pre-processing operation will be approved with on the original patterns [10][11][12]. Depending on [13][14][15][16] deep learning techniques have been supported in image classification and responsibilities recovery. The usage vein recognition approach from side to side Deep learning DL-based and CNN architecture applications.…”

Section: Introductionmentioning

confidence: 99%

Improvement of confusion matrix for Hand Vein Recognition Based On Deep- Learning multi-classifier Decisions

Nawwar

Kasban

Salama

2021

Arab Journal of Nuclear Sciences and Applications

View full text Add to dashboard Cite

“…Penelitian selanjutnya dilakukan oleh (Bhagat et al, 2017) penelitian ini menggunakan GLCM, LBP dan Gabor Wavelet sebagai metode ekstraksi ciri, dan menggunakan SVM sebagai metode identifikasinya.…”

unclassified

Penerapan Metode Pembelajaran Menggunakan Ekstraksi Fitur dan Algoritma Klasifikasi untuk Identifikasi Pengenalan Iris

Hidayat

Agustiani

Wildah

et al. 2021

View full text Add to dashboard Cite

Iris mata terletak di antara kornea mata dan lensa mata, yang berfungsi untuk mengontrol intensitas atau jumlah cahaya yang masuk dengan cara melebarkan dan mengecilkan pupil. Setiap orang memiliki iris yang berbeda dan memiliki stabilitas sepanjang hidup, kecuali terjadi kerusakan yang tidak disengaja pada iris seperti terjadi kecelakaan. Tujuan dari penelitian ini adalah untuk melakukan pengklasifikasian dan identifikasi pengenalan citra iris dengan menggunakan metode pembelajaran atau machine learning. Metode yang diusulkan dalam penelitian ini adalah penerapan ekstraksi fitur seperti HOG, Hu-Moments, dan Haralick dengan algoritma klasifikasi yang terdiri dari LR, LDA, KNN, RF, CART, NB, dan SVM. Berdasarkan hasil pengujian yang telah dilakukan dalam mengklasifikasikan iris dapat disimpulkan bahwa penggunaan ekstraksi fitur sangat berpengaruh pada nilai akurasi yang dihasilkan. Dalam hal ini nilai akurasi terbaik diperoleh dari penggabungan ekstraksi fitur HOG dan haralick pada algoritma Random Forest dengan nilai akurasi sebesar 81.38℅.

show abstract

Iris recognition using radon transform and GLCM

Cited by 5 publications

References 6 publications

Achieving Information Security by multi-Modal Iris-Retina Biometric Approach Using Improved Mask R-CNN

Achieving Information Security by multi-Modal Iris-Retina Biometric Approach Using Improved Mask R-CNN

Improvement of confusion matrix for Hand Vein Recognition Based On Deep- Learning multi-classifier Decisions

Penerapan Metode Pembelajaran Menggunakan Ekstraksi Fitur dan Algoritma Klasifikasi untuk Identifikasi Pengenalan Iris

Contact Info

Product

Resources

About