INDIVIDUAL IDENTIFICATION BY IRIS USING HISTOGRAM OF ORIENTED GRADIENT (HOG) AND BACKPROPAGATION NEURAL NETWORK

Widya Alisya  Kusuma Ningrum; Iwan Iwut  Tritoasmoro; Sofia Saidah

doi:10.52436/1.jutif.2023.4.2.558

Widya Alisya Kusuma Ningrum Program Studi S1 Teknik Telekomunikasi, Fakultas Teknik Elektro, Universitas Telkom, Indonesia
Iwan Iwut Tritoasmoro Program Studi S1 Teknik Telekomunikasi, Fakultas Teknik Elektro, Universitas Telkom, Indonesia
Sofia Saidah Program Studi S1 Teknik Telekomunikasi, Fakultas Teknik Elektro, Universitas Telkom, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2023.4.2.558

Keywords: Backpropagation Neural Network (BPNN), Biometrics, Histogram of Oriented Gradient, Iris eye’s.

Abstract

The eye’s iris biometrics is a type of biometric for individual identification that is more stable than other types of biometrics because a person's iris eye’s has a delicate fiber pattern and unique characteristics. Especially with the rapid development of the times, the need for identity recognition systems is also increasing. Introducing individuals in traditional ways is still less effective than biometric systems because, compared to conventional methods, biometric systems are safer and are not easily stolen, imitated, or accessed by any unauthorized person. In this research has been carried out by designing a simulation system for individual identification through iris eyes images using the Histogram of Oriented Gradien (HOG) method for image extraction. They were continued with classification using Artificial Neural Network (ANN) Backpropagation. The dataset used is primary data taken directly through smartphone cameras from 30 individuals.Based on the test results and analysis of the Histogram of Oriented Gradien method using an image size of 128×128 pixels, parameters of Cell Size 16×16 cells, Bins Numbers 12, Size Block 2×2 cells, L2-Hys normalization scheme, and JST backpropagation classification with Random state value 1, Learning Rates 0.001, Epoch 200, Hidden Layer 100 with the system's sigmoid activation function can produce a performance system with the most significant performance accuracy of 91.93% , using 1500 training data and 1500 iris eyes image test data.

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