Feature Extraction Methods On A Partial Section Of The Iris Region For Iris Classification
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Date
2022-02-01
Authors
Ali, Ahmad Nazri
Journal Title
Journal ISSN
Volume Title
Publisher
Universiti Sains Malaysia
Abstract
Iris classification is a biometric system to classify a person using the
individual’s iris pattern. One of the important steps in this system is to extract the iris
information from the segmented iris region. Although several methods have
produced a perfect recognition rate, they require intensive processing that involves
the process of isolating the iris information as well as other information such as
eyelid and eyelashes during template generation. The process of separating these two
parts is crucially needed so that no eyelid or eyelash data are acknowledged as iris
data during matching. To define the issue, the widely used approach of the feature
extraction method as proposed by Daugman is studied in this research work. Then,
an alternative feature extraction technique by using the upper half of the iris region
that is able to skip the process of separating between iris information and eyelids or
eyelashes during feature computation is proposed which is not only able to reduce
the computation time but is able to preserve the accuracy rate. The proposed schemes
are based on difference cumulative bin (DCB), sequential cumulative bin (SCB) and
overlap mean intensity (OMI) that utilize the local texture analysis computation for
transforming pixel value to a binary bit. The methods are assessed using Support
Vector Machines (SVM), k-NN and Naïve Bayes classifiers on various region sizes
and neighbourhood elements. The result showed that although the average accuracy
for the proposed methods on individual assessment (94.27%) was slightly lower than
by the Daugman method (95.77%), the classification rate for the proposed methods
has improved to achieve 96.26% accuracy if the assessment uses a concatenated
mode set of features and also has managed to reduce the computation time which is
0.030 ms compared to Daugman’s method that required 0.166 ms.