Open AccessDetection of Iris Localization in Facial Images Using Haar Cascade Circular Hough Transform
Author(s) -
Haider Shamil,
Bassam Al Kindy,
Amel H. Abbas
Publication year - 2020
Publication title -
xi'nan jiaotong daxue xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 21
ISSN - 0258-2724
DOI - 10.35741/issn.0258-2724.55.4.5
Subject(s) - haar like features , hough transform , artificial intelligence , adaboost , iris recognition , computer vision , computer science , haar , classifier (uml) , cascade , pattern recognition (psychology) , iris (biosensor) , cascading classifiers , face detection , face (sociological concept) , feature extraction , facial recognition system , image (mathematics) , biometrics , engineering , social science , random subspace method , chemical engineering , sociology , wavelet
In numerous science applications, face detection and iris extraction have been recognized as crucial stages by getting more consideration among researchers as it has an important job. This paper presents an automatic detection method of the iris and its center detection by applying the Haar Cascade Classifier and the Circular Hough Transform algorithm. The suggested method is divided into two primary methodologies: face recognition utilizes the Haar Cascade Classifier and iris extraction using the Hough Transform. The system detects the face from a set of facial images using an Impa-faced dataset. The improved AdaBoost algorithm constructs a cascaded classifier for face detection. Then, by applying the Haar Cascade to obtain an eye pair region and a Hough transform for iris detection by extracting Haar features. Finally, the improved circular Hough transform algorithm locates the iris center. The experimental results of the suggested method show a high-speed, robust ability to acquire the coordinates of the iris center accurately under various illumination changes on different states of human images. The overall accuracy for locating the iris center was 98.75%.