An approach to human iris recognition using quantitative analysis of image features and machine learning

Autor: Donya Khaledyan, Najmeh Mashhadi, Morteza Heidari, Abolfazl Zargari Khuzani
Jazyk: angličtina
Rok vydání: 2020
Předmět:
FOS: Computer and information sciences
Computer Science - Machine Learning
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Feature extraction
Fast Fourier transform
Iris recognition
0211 other engineering and technologies
Computer Science - Computer Vision and Pattern Recognition
02 engineering and technology
urologic and male genital diseases
Machine Learning (cs.LG)
Wavelet
FOS: Electrical engineering
electronic engineering
information engineering
0202 electrical engineering
electronic engineering
information engineering
Segmentation
cardiovascular diseases
021106 design practice & management
Artificial neural network
business.industry
urogenital system
Image and Video Processing (eess.IV)
fungi
Pattern recognition
Electrical Engineering and Systems Science - Image and Video Processing
female genital diseases and pregnancy complications
Feature (computer vision)
020201 artificial intelligence & image processing
IRIS (biosensor)
Artificial intelligence
business
Zdroj: GHTC
Popis: The Iris pattern is a unique biological feature for each individual, making it a valuable and powerful tool for human identification. In this paper, an efficient framework for iris recognition is proposed in four steps. (1) Iris segmentation (using a relative total variation combined with Coarse Iris Localization), (2) feature extraction (using Shape&density, FFT, GLCM, GLDM, and Wavelet), (3) feature reduction (employing Kernel-PCA) and (4) classification (applying multi-layer neural network) to classify 2000 iris images of CASIA-Iris-Interval dataset obtained from 200 volunteers. The results confirm that the proposed scheme can provide a reliable prediction with an accuracy of up to 99.64%.
Databáze: OpenAIRE
Externí odkaz: